PubMed Search MCP

Professional Literature Research Assistant for AI Agents - More than just an API wrapper

A Domain-Driven Design (DDD) based MCP server that serves as an intelligent research assistant for AI agents, providing task-oriented literature search and analysis capabilities.

✨ What's Included:

🔧 46 MCP Tools - Streamlined PubMed, Europe PMC, CORE, NCBI database access, and Research Timeline / Context Graph
🖼️ OA Figure Extraction - Pull figure captions, direct image URLs, and PDF links from PMC Open Access articles
📘 Docs Site - Browse language-switchable user and developer guides, architecture, quick reference, pipeline tutorials, source contracts, troubleshooting, and deployment in one place at u9401066.github.io/pubmed-search-mcp
📖 GitHub Wiki - GitHub-native mirror of the same canonical documentation at github.com/u9401066/pubmed-search-mcp/wiki
📚 24 Claude Skills - Ready-to-use workflow guides for AI agents (Claude Code-specific)
📖 Copilot Instructions - VS Code GitHub Copilot integration guide

🌐 Language: English | 繁體中文

📘 Documentation Map: README is the quick project entry point. Use the Docs Site for the best reading experience, the GitHub Wiki for GitHub-native navigation, and source docs for edits: User guide | Advanced workflows | Capability-first guide | Developer guide | Complete index

🚀 Quick Install

Prerequisites

Python 3.10+ — Download

uv (recommended) — Install uv

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

NCBI Email — Required by NCBI API policy. Any valid email address.
NCBI API Key (optional) — Get one here for higher rate limits (10 req/s vs 3 req/s)
OpenAlex API Key (optional) — set OPENALEX_API_KEY to use authenticated OpenAlex requests instead of mailto-only polite-pool auth

Install & Run

# Option 1: Zero-install with uvx (recommended for trying out)
uvx pubmed-search-mcp

# Option 2: Add as project dependency
uv add pubmed-search-mcp

# Option 3: pip install
pip install pubmed-search-mcp

Python SDK Facade

For in-process Python integrations, use the stable SDK facade instead of importing MCP tool modules:

from pubmed_search.api import PubMedSearchClient, PubMedSearchConfig

client = PubMedSearchClient(PubMedSearchConfig(email="your@email.com"))
result = await client.unified_search("remimazolam ICU sedation", limit=20)

print(result.articles)
print(result.source_counts)
print(result.artifact)  # artifact locator when persistence is enabled

Use uvx pubmed-search-mcp or /mcp for agent tool discovery. Use the SDK for Python package/notebook calls where a typed object is easier than parsing an MCP response string.

⚙️ Configuration

This MCP server works with any MCP-compatible AI tool. Choose your preferred client:

VS Code / Cursor (`.vscode/mcp.json`)

{
  "servers": {
    "pubmed-search": {
      "type": "stdio",
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com"
      }
    }
  }
}

Optional: enable browser-session PDF fallback once and let tools auto-use it:

{
  "servers": {
    "pubmed-search": {
      "type": "stdio",
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com",
        "BROWSER_FETCH_CONFIG": "{\"enabled\":true,\"auto_enabled\":true,\"broker_url\":\"http://127.0.0.1:8766/fetch\",\"token\":\"local-dev-token\",\"allowed_hosts\":[\"jamanetwork.com\",\"*.jamanetwork.com\",\"nejm.org\",\"*.nejm.org\"]}"
      }
    }
  }
}

With this setting, get_fulltext will automatically try the local broker for institutional or publisher landing pages. Pass allow_browser_session=false only when you want to suppress it for a specific call.

Run the local broker with download interception:

uv sync --extra browser-broker
uv run playwright install chromium
uv run pubmed-browser-fetch-broker --token local-dev-token

The broker launches a persistent browser profile with download interception enabled. Log in once inside that broker-controlled browser window, and subsequent PDF downloads will be captured automatically without a native "Save As" dialog.

Claude Desktop (`claude_desktop_config.json`)

{
  "mcpServers": {
    "pubmed-search": {
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com"
      }
    }
  }
}

Config file location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/Claude/claude_desktop_config.json

Claude Code

claude mcp add pubmed-search -- uvx pubmed-search-mcp

Or add to .mcp.json in your project root:

{
  "mcpServers": {
    "pubmed-search": {
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com"
      }
    }
  }
}

Zed AI (`settings.json`)

Zed editor (z.ai) supports MCP servers natively. Add to your Zed settings.json:

{
  "context_servers": {
    "pubmed-search": {
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com"
      }
    }
  }
}

Tip: Open Command Palette → zed: open settings to edit, or go to Agent Panel → Settings → "Add Custom Server".

OpenClaw 🦞 (`~/.openclaw/openclaw.json`)

OpenClaw uses MCP servers via the mcp-adapter plugin. Install the adapter first:

openclaw plugins install mcp-adapter

Then add to ~/.openclaw/openclaw.json:

{
  "plugins": {
    "entries": {
      "mcp-adapter": {
        "enabled": true,
        "config": {
          "servers": [
            {
              "name": "pubmed-search",
              "transport": "stdio",
              "command": "uvx",
              "args": ["pubmed-search-mcp"],
              "env": {
                "NCBI_EMAIL": "your@email.com"
              }
            }
          ]
        }
      }
    }
  }
}

Restart the gateway after configuration:

openclaw gateway restart
openclaw plugins list  # Should show: mcp-adapter | loaded

Cline (`cline_mcp_settings.json`)

{
  "mcpServers": {
    "pubmed-search": {
      "command": "uvx",
      "args": ["pubmed-search-mcp"],
      "env": {
        "NCBI_EMAIL": "your@email.com",
        "S2_API_KEY": "your_semantic_scholar_key",
        "PUBMED_SEARCH_DISABLED_SOURCES": ""
      },
      "alwaysAllow": [],
      "disabled": false
    }
  }
}

Other MCP Clients

Any MCP-compatible client can use this server via stdio transport:

# Command
uvx pubmed-search-mcp

# With environment variable
NCBI_EMAIL=your@email.com uvx pubmed-search-mcp

Note: NCBI_EMAIL is required by NCBI API policy. Optionally set NCBI_API_KEY for higher rate limits (10 req/s vs 3 req/s). 📖 Detailed Integration Guides: See docs/INTEGRATIONS.md for all environment variables, Copilot Studio setup, Docker deployment, proxy configuration, and troubleshooting.

🎯 Design Philosophy

Core Positioning: The intelligent middleware between AI Agents and academic search engines.

Why This Server?

Other tools give you raw API access. We give you vocabulary translation + intelligent routing + research analysis:

Challenge	Our Solution
Agent uses ICD codes, PubMed needs MeSH	✅ Auto ICD→MeSH conversion
Multiple databases, different APIs	✅ Unified Search single entry point
Clinical questions need structured search	✅ PICO handoff + pipeline (`parse_pico` validates agent-provided P/I/C/O and returns a runnable `template: pico` pipeline)
Typos in medical terms	✅ ESpell auto-correction
Too many results from one source	✅ Parallel multi-source with dedup
Need to trace research evolution	✅ Research Timeline & Tree with landmark detection, diagnostics, and sub-topic branching
Citation context is unclear	✅ Citation Tree forward/backward/network
Can't access full text	✅ Multi-source fulltext (Europe PMC XML, Unpaywall OA locations, institutional direct/EZproxy, CORE, and downloader fallbacks)
Gene/drug info scattered across DBs	✅ NCBI Extended (Gene, PubChem, ClinVar)
Need cutting-edge preprints	✅ Preprint search (arXiv, medRxiv, bioRxiv) with peer-review filtering
Export to reference managers	✅ One-click export (official RIS/MEDLINE/CSL JSON; local RIS/BibTeX/CSV/MEDLINE/JSON)

Key Differentiators

Vocabulary Translation Layer - Agent speaks naturally, we translate to each database's terminology (MeSH, ICD-10, text-mined entities)
Unified Search Gateway - One unified_search() call, auto-dispatch to PubMed/Europe PMC/CORE/OpenAlex
PICO Handoff + Pipeline - the Agent extracts P/I/C/O, parse_pico() validates that structured handoff, and the backend template: pico pipeline executes O-aware precision/recall searches
Research Timeline & Lineage Tree - Detect milestones with policy-driven heuristics, identify landmark papers via multi-signal scoring, surface timeline diagnostics, and visualize research evolution as branching trees by sub-topic
Citation Network Analysis - Build multi-level citation trees to map an entire research landscape from a single paper
Full Research Lifecycle - From search → discovery → full text → analysis → export, all in one server
Agent-First Design - Output optimized for machine decision-making, not human reading

📡 External APIs & Data Sources

This MCP server integrates with multiple academic databases and APIs:

Core Data Sources

Source	Coverage	Vocabulary	Auto-Convert	Description
NCBI PubMed	36M+ articles	MeSH	✅ Native	Primary biomedical literature
NCBI Entrez	Multi-DB	MeSH	✅ Native	Gene, PubChem, ClinVar
Europe PMC	33M+	Text-mined	✅ Extraction	Full text XML access
CORE	200M+	None	➡️ Free-text	Open access aggregator
Semantic Scholar	200M+	S2 Fields	➡️ Free-text	AI-powered recommendations
OpenAlex	250M+	Concepts	➡️ Free-text	Open scholarly metadata
NIH iCite	PubMed	N/A	N/A	Citation metrics (RCR)

🔑 Key: ✅ = Full vocabulary support | ➡️ = Query pass-through (no controlled vocabulary)

ICD Codes: Auto-detected and converted to MeSH before PubMed search

Environment Variables

# Required
NCBI_EMAIL=your@email.com          # Required by NCBI policy

# Optional - For higher rate limits
NCBI_API_KEY=your_ncbi_api_key     # Get from: https://www.ncbi.nlm.nih.gov/account/settings/
CORE_API_KEY=your_core_api_key     # Get from: https://core.ac.uk/services/api
CROSSREF_EMAIL=your@email.com      # CrossRef polite pool
UNPAYWALL_EMAIL=your@email.com     # Unpaywall OA resolver
S2_API_KEY=your_s2_api_key         # Alias: SEMANTIC_SCHOLAR_API_KEY
PUBMED_SEARCH_DISABLED_SOURCES=    # Example: semantic_scholar

# Optional - Network settings
HTTP_PROXY=http://proxy:8080       # HTTP proxy for API requests
HTTPS_PROXY=https://proxy:8080     # HTTPS proxy for API requests

# Optional - Institutional fulltext access
INSTITUTIONAL_DIRECT_FETCH=true    # Try DOI publisher pages before CORE fallback
EZPROXY_ENABLED=false              # Enable only after configuring EZPROXY_HOST + cookie
EZPROXY_HOST=ezproxy.example.edu
EZPROXY_COOKIE_FILE=/path/to/cookies.json

# Optional - Local note export
PUBMED_NOTES_DIR=/path/to/wiki/references  # save_literature_notes target folder
PUBMED_WORKSPACE_DIR=/path/to/project       # fallback: references/ under this workspace
PUBMED_DATA_DIR=~/.pubmed-search-mcp        # fallback: references/ under this data dir

Local note export resolves directories in this order: output_dir argument, PUBMED_NOTES_DIR, PUBMED_WORKSPACE_DIR/references, PUBMED_DATA_DIR/references, then ~/.pubmed-search-mcp/references. For LLM wiki compatibility, wiki and foam exports use stable link targets based on PMID, DOI, PMCID, or fallback identifiers; titles remain aliases/display labels, and the response includes wiki_validation for unresolved wikilink checks.

🔄 How It Works: The Middleware Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                              AI AGENT                                        │
│                                                                              │
│   "Find papers about I10 hypertension treatment in diabetic patients"       │
│                                                                              │
└─────────────────────────────────┬───────────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                     🔄 PUBMED SEARCH MCP (MIDDLEWARE)                        │
│  ┌─────────────────────────────────────────────────────────────────────────┐│
│  │  1️⃣ VOCABULARY TRANSLATION                                              ││
│  │     • ICD-10 "I10" → MeSH "Hypertension"                                ││
│  │     • "diabetic" → MeSH "Diabetes Mellitus"                             ││
│  │     • ESpell: "hypertention" → "hypertension"                           ││
│  └─────────────────────────────────────────────────────────────────────────┘│
│  ┌─────────────────────────────────────────────────────────────────────────┐│
│  │  2️⃣ INTELLIGENT ROUTING                                                 ││
│  │     ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐             ││
│  │     │ PubMed   │  │Europe PMC│  │   CORE   │  │ OpenAlex │             ││
│  │     │  36M+    │  │   33M+   │  │  200M+   │  │  250M+   │             ││
│  │     │  (MeSH)  │  │(fulltext)│  │  (OA)    │  │(metadata)│             ││
│  │     └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘             ││
│  │          └──────────────┴──────────────┴──────────────┘                 ││
│  │                              ▼                                          ││
│  │  3️⃣ RESULT AGGREGATION: Dedupe + Rank + Enrich                         ││
│  └─────────────────────────────────────────────────────────────────────────┘│
└─────────────────────────────────┬───────────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                         UNIFIED RESULTS                                      │
│   • 150 unique papers (deduplicated from 4 sources)                          │
│   • Ranked by relevance + citation impact (RCR)                              │
│   • Full text links enriched from Europe PMC                                 │
└─────────────────────────────────────────────────────────────────────────────┘

🛠️ MCP Tools Overview

If you want to understand the tool surface as a usable system, do not start by memorizing 46 tool names.

Start with the Tools Usage Guide: it compresses the current 46 tools into 8 capability families, explains the theoretical lower bound, and gives intent-based routing for both humans and agents.

🔍 Search & Query Intelligence

┌─────────────────────────────────────────────────────────────────┐
│                      SEARCH ENTRY POINT                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│   unified_search()          ← 🌟 Single entry for all sources    │
│        │                                                         │
│        ├── Quick search     → Direct multi-source query          │
│        ├── PICO hints       → Detects comparison, shows P/I/C/O  │
│        └── ICD expansion    → Auto ICD→MeSH conversion           │
│                                                                  │
│   Sources: PubMed · Europe PMC · CORE · OpenAlex                 │
│   Auto: Deduplicate → Rank → Enrich full-text links              │
│                                                                  │
├─────────────────────────────────────────────────────────────────┤
│   QUERY INTELLIGENCE                                             │
│                                                                  │
│   generate_search_queries() → MeSH expansion + synonym discovery │
│   parse_pico()              → Agent-provided PICO handoff        │
│   analyze_search_query()    → Query analysis without execution   │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

🔬 Discovery Tools (After Finding Key Papers)

                        Found important paper (PMID)
                                   │
           ┌───────────────────────┼───────────────────────┐
           │                       │                       │
           ▼                       ▼                       ▼
    ┌─────────────┐        ┌─────────────┐        ┌─────────────┐
    │  BACKWARD   │        │  SIMILAR    │        │  FORWARD    │
    │  ◀──────    │        │  ≈≈≈≈≈≈     │        │  ──────▶    │
    │             │        │             │        │             │
    │ get_article │        │find_related │        │find_citing  │
    │ _references │        │ _articles   │        │ _articles   │
    │             │        │             │        │             │
    │ Foundation  │        │  Similar    │        │ Follow-up   │
    │  papers     │        │   topic     │        │  research   │
    └─────────────┘        └─────────────┘        └─────────────┘

    fetch_article_details()   → Detailed article metadata
    get_citation_metrics()    → iCite RCR, citation percentile
    build_citation_tree()     → Full network visualization (6 formats)

📚 Full Text, Figure Extraction & Export

Category	Tools
Full Text	`get_fulltext` → Europe PMC XML when a PMCID is available; DOI-backed Unpaywall, institutional direct/EZproxy, CORE, and downloader fallbacks when needed
Figures	`get_article_figures` → Extract figure labels, captions, image URLs, and PDF links from PMC Open Access articles
Figure-aware Full Text	`get_fulltext(include_figures=True)` → Embed figure metadata alongside structured fulltext
Text Mining	`get_text_mined_terms` → Extract genes, diseases, chemicals
Export	`prepare_export` → official RIS/MEDLINE/CSL JSON or local RIS/BibTeX/CSV/MEDLINE/JSON; `save_literature_notes` → local wiki/Foam-compatible/Markdown/MedPaper-style notes plus collection-level CSL JSON

🖼️ OA Figure-First Exploration

Use the PMC Open Access path when an agent needs evidence figures, not just article text:

get_article_figures(identifier="PMC12086443") → Figure labels, captions, image URLs, and PDF/article links
get_fulltext(pmcid="PMC7096777", include_figures=True) → Structured fulltext with figures inline
Figure output preserves article context, so agents can connect each figure back to the sections where it is mentioned

🧬 NCBI Extended Databases

Tool	Description
`search_gene`	Search NCBI Gene database
`get_gene_details`	Gene details by NCBI Gene ID
`get_gene_literature`	PubMed articles linked to a gene
`search_compound`	Search PubChem compounds
`get_compound_details`	Compound details by PubChem CID
`get_compound_literature`	PubMed articles linked to a compound
`search_clinvar`	Search ClinVar clinical variants

🕰️ Research Timeline & Lineage Tree

Tool	Description
`build_research_timeline`	Build timeline/tree with landmark detection and formatted diagnostics. Output: text, tree, mermaid, mindmap, json, json_tree, timeline_js, d3
`analyze_timeline_milestones`	Analyze milestone distribution with diagnostics payload
`compare_timelines`	Compare multiple topic timelines with per-topic diagnostics

Current timeline and tree outputs are projections, not a persisted chronicle asset. The planned persistent/versioned Research Chronicle is specified in docs/RESEARCH_CHRONICLE_REFACTOR_SPEC.md.

🏥 Institutional Access & ICD Conversion

Tool	Description
`configure_institutional_access`	Configure institution's link resolver
`get_institutional_link`	Generate OpenURL access link
`list_resolver_presets`	List resolver presets
`test_institutional_access`	Test resolver configuration
`diagnose_institutional_access`	Diagnose direct DOI, EZproxy, and OpenURL handoff paths
`convert_icd_mesh`	Convert between ICD codes and MeSH terms (bidirectional)
`unified_search`	Auto-detect ICD codes in queries and expand them to MeSH

💾 Session Management

Tool	Description
`get_session_pmids`	Retrieve cached PMID lists
`get_cached_article`	Get article from session cache (no API cost)
`get_session_summary`	Session status overview
`read_session`	Facade for PMIDs, cached articles, history, and persistent artifacts

Dynamic MCP resources are also available for agents that can read resources directly:

session://context — active session status
session://last-search — latest search metadata
session://last-search/pmids — latest PMID list + CSV form
session://last-search/results — cached article payloads for the latest search

Persistent Artifacts

Persistent MCP output artifacts are saved for reusable unified_search and get_fulltext responses when session persistence is configured. Tool responses act like index cards: they include enough counts, source warnings, and artifact hints for an agent to answer immediately, while the full evidence payload stays in files that can be read repeatedly. The compact artifact locator includes artifact_id, artifact_uri, primary_file, summary, file inventory, read_order, audit status, and exact read_session(...) retrieval hints. Set PUBMED_ARTIFACT_INCLUDE_LOCAL_PATHS=true only when a local MCP client should also receive local_path and manifest_path directly.

Remote clients that cannot read the server filesystem can retrieve the same content through the session facade:

read_session(action="list_artifacts")
read_session(action="artifact", artifact_id="...")
read_session(action="artifact", artifact_uri="artifact://...")
read_session(action="artifact", artifact_uri="artifact://...", artifact_file="audit.json")
read_session(action="artifact", artifact_uri="artifact://...", artifact_file="query_strategy.json")
read_session(action="artifact", artifact_uri="artifact://...", artifact_file="results.json", offset=0, max_chars=200000)
read_session(action="list_artifacts", include_local_paths=true)

unified_search artifacts use a research envelope. Start with audit.json for source-count and completeness warnings, then query_strategy.json for the exact executed plan, and finally results.json / results.toon for the complete article list. This keeps MCP response tokens small without losing academic traceability.

Artifacts are generated from the already-computed result object, so reading an artifact does not rerun searches or fulltext retrieval. read_session redacts local filesystem paths by default; local_path and manifest_path are server-local paths, not portable client paths. Artifacts from get_fulltext may contain article body text, including subscription or institutionally accessed content. Store and share them according to publisher, license, and institutional access terms. Large get_fulltext responses are returned inline as a preview when an artifact is available; use the artifact locator to retrieve the saved full content.

When one source fails but the overall search can continue, JSON responses may include source_errors; markdown responses show a Source warnings line. For Semantic Scholar HTTP 429s, set S2_API_KEY / SEMANTIC_SCHOLAR_API_KEY, retry later, or temporarily exclude it with sources="auto,-semantic_scholar" or PUBMED_SEARCH_DISABLED_SOURCES=semantic_scholar.

Pipeline Management

manage_pipeline is the primary facade for pipeline CRUD, history, and scheduling. The more specific pipeline tools remain available as compatibility wrappers.

Tool	Description
`manage_pipeline`	Primary facade for save, list, load, delete, history, and schedule actions
`save_pipeline`	Save a pipeline config for later reuse (YAML/JSON, auto-validated)
`list_pipelines`	List saved pipelines (filter by tag/scope)
`load_pipeline`	Load pipeline from name or file for review/editing
`delete_pipeline`	Delete pipeline and its execution history
`get_pipeline_history`	View execution history with article diff analysis
`schedule_pipeline`	Create, update, or remove recurring pipeline schedules

Step-by-step tutorials:

English: docs/PIPELINE_MODE_TUTORIAL.en.md
繁體中文: docs/PIPELINE_MODE_TUTORIAL.md

👁️ Vision & Image Search

Tool	Description
`analyze_figure_for_search`	Handoff an uploaded image, image URL, or data URI to agent vision for search-term extraction
`search_biomedical_images`	Search biomedical images across Open-i (X-ray, microscopy, photos, diagrams)

Use analyze_figure_for_search when the user supplies an image and the agent must interpret its meaning first. The tool returns MCP ImageContent plus instructions for the LLM agent to extract English biomedical terms, then continue with search_biomedical_images for similar Open-i images or unified_search for related papers.

📄 Preprint Search

Search arXiv, medRxiv, and bioRxiv preprint servers via unified_search options flags:

preprints: Search preprint servers and merge preprints into the main aggregated result set with article_type=PREPRINT.
all_types: Keep non-peer-reviewed content already returned by selected scholarly sources even without a preprint-server crawl.

Recommended combinations:

Empty options: Peer-reviewed results only; preprint-like records are filtered.
options="preprints": Searches arXiv, medRxiv, and bioRxiv, then ranks/dedupes those preprints with the main results.
options="preprints, all_types": Same preprint-server crawl, plus other non-peer-reviewed records from selected sources are retained.
options="all_types": No preprint-server crawl, but non-peer-reviewed items from searched sources are retained.

Preprint detection — articles are identified as preprints by:

Article type from source API (OpenAlex, CrossRef, Semantic Scholar)
arXiv ID present without PubMed ID
Known preprint server source or journal name
DOI prefix matching preprint servers (e.g., 10.1101/ → bioRxiv/medRxiv, 10.48550/ → arXiv)

🌳 Research Context Graph

unified_search can append a lightweight research lineage view built from PMID-backed ranked results:

Option Flag	Description
`context_graph`	Append a lightweight Research Context Graph preview from the current PMID-backed ranked set to Markdown output and include `research_context` in JSON output

This is useful when an agent needs quick thematic branching without making a second build_research_timeline call.

📊 Count-First Orientation

unified_search can also front-load the existing source coverage and decision hints for agents that want routing help before reading the ranked list:

Option Flag	Description
`counts_first`	Add a source-count table, coverage summary, and next-tool recommendations to the response

Example:

unified_search(query="remimazolam ICU sedation", options="counts_first")

This mode is useful when the agent should decide whether to expand a source, inspect the lead PMID, fetch fulltext, extract figures, or pivot into timeline exploration.

⏱️ MCP Progress Reporting

When the MCP client provides a progress token, unified_search, build_research_timeline, analyze_timeline_milestones, compare_timelines, get_fulltext, and get_text_mined_terms emit progress updates for their major phases. This reduces the "black box" wait time for agents during longer searches.

📋 Agent Usage Examples

1️⃣ Quick Search (Simplest)

# Agent just asks naturally - middleware handles everything
unified_search(query="remimazolam ICU sedation", limit=20)

# Or with clinical codes - auto-converted to MeSH
unified_search(query="I10 treatment in E11.9 patients")
#                     ↑ ICD-10           ↑ ICD-10
#                     Hypertension       Type 2 Diabetes

2️⃣ PICO Clinical Question

Simple path — unified_search can search directly (no PICO decomposition):

# unified_search searches as-is; detects "A vs B" pattern and shows PICO hints in metadata
unified_search(query="Is remimazolam better than propofol for ICU sedation?")
# → Multi-source keyword search + PICO hint metadata in output
# ⚠️ This does NOT auto-decompose PICO or expand MeSH!
# For structured PICO search, use the Agent workflow below

Agent workflow — agent-provided PICO + backend pipeline search (recommended for clinical questions):

┌─────────────────────────────────────────────────────────────────────────┐
│  "Is remimazolam better than propofol for ICU sedation?"                │
└─────────────────────────────────┬───────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────────┐
│                         parse_pico()                                     │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐                     │
│  │    P    │  │    I    │  │    C    │  │    O    │                     │
│  │  ICU    │  │remimaz- │  │propofol │  │sedation │                     │
│  │patients │  │  olam   │  │         │  │outcomes │                     │
│  └────┬────┘  └────┬────┘  └────┬────┘  └────┬────┘                     │
└───────┼────────────┼────────────┼────────────┼──────────────────────────┘
        │            │            │            │
        ▼            ▼            ▼            ▼
┌─────────────────────────────────────────────────────────────────────────┐
│              generate_search_queries() × 4 (parallel)                    │
│                                                                          │
│  P → "Intensive Care Units"[MeSH]                                        │
│  I → "remimazolam" [Supplementary Concept], "CNS 7056"                   │
│  C → "Propofol"[MeSH], "Diprivan"                                        │
│  O → "Conscious Sedation"[MeSH], "Deep Sedation"[MeSH]                   │
└─────────────────────────────────┬───────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────────┐
│              Agent combines with Boolean logic                           │
│                                                                          │
│  (P) AND (I) AND (C) AND (O)  ← High precision                           │
│  (P) AND (I OR C) AND (O)     ← High recall                              │
└─────────────────────────────────┬───────────────────────────────────────┘
                                  │
                                  ▼
┌─────────────────────────────────────────────────────────────────────────┐
│              unified_search() (auto multi-source + dedup)                │
│                                                                          │
│  PubMed + Europe PMC + CORE + OpenAlex → Auto deduplicate & rank         │
└─────────────────────────────────────────────────────────────────────────┘

# Step 1: Agent extracts P/I/C/O, then validates the structured handoff
pico = parse_pico(
    description="Is remimazolam better than propofol for ICU sedation?",
    p="ICU patients requiring sedation",
    i="remimazolam",
    c="propofol",
    o="sedation efficacy, delirium, hypotension"
)
# Returns validation plus a ready-to-run `template: pico` pipeline.

# Step 2: Get MeSH for each element (parallel!)
generate_search_queries(topic="ICU patients")   # P
generate_search_queries(topic="remimazolam")    # I
generate_search_queries(topic="propofol")       # C
generate_search_queries(topic="sedation")       # O

# Step 3: Either pass expanded fragments back as p_query/i_query/c_query/o_query
# or let the backend pipeline use the structured P/I/C/O labels.

# Step 4: Search (backend runs O-aware precision/recall searches, dedup, rank)
unified_search(
    query="Is remimazolam better than propofol for ICU sedation?",
    pipeline=pico["pipeline"]
)

3️⃣ Explore from Key Paper

# Found landmark paper PMID: 33475315
find_related_articles(pmid="33475315")   # Similar methodology
find_citing_articles(pmid="33475315")    # Who built on this?
get_article_references(pmid="33475315")  # What's the foundation?

# Build complete research map
build_citation_tree(pmid="33475315", depth=2, output_format="mermaid")

4️⃣ Gene/Drug Research

# Research a gene
search_gene(query="BRCA1", organism="human")
get_gene_literature(gene_id="672", limit=20)

# Research a drug compound
search_compound(query="propofol")
get_compound_literature(cid="4943", limit=20)

5️⃣ Export Results

# Export last search results
prepare_export(pmids="last", format="ris")      # → EndNote/Zotero
prepare_export(pmids="last", format="bibtex", source="local")  # → LaTeX
prepare_export(pmids="last", format="csl")      # → CSL JSON from the official NCBI Citation API
save_literature_notes(pmids="last")              # → local wiki note + Foam-compatible wikilinks + CSL JSON
save_literature_notes(pmids="last", note_format="medpaper", output_dir="./references")
save_literature_notes(pmids="last", template_file="./reference-template.md")

# Retrieve full text for a selected paper from the last search
get_fulltext(pmid="12345678", extended_sources=True)

6️⃣ Preprint Search

# Include preprints alongside peer-reviewed results
unified_search(query="COVID-19 vaccine efficacy", options="preprints")
# → Main aggregated results include labelled arXiv, medRxiv, and bioRxiv preprints

# Include preprints and retain non-peer-reviewed items in main results
unified_search(query="CRISPR gene therapy", options="preprints, all_types")
# → Preprint-server crawl + non-peer-reviewed items retained in main results

# Only peer-reviewed (default behavior)
unified_search("diabetes treatment")
# → Preprints from any source automatically filtered out

# Add a research context graph preview to the same search response
unified_search("remimazolam ICU sedation", options="context_graph")

7️⃣ Pipeline (Reusable Search Plans)

# Save a template-based pipeline through the primary facade
manage_pipeline(
  action="save",
    name="icu_sedation_weekly",
    config="template: pico\nparams:\n  P: ICU patients\n  I: remimazolam\n  C: propofol\n  O: delirium",
    tags="anesthesia,sedation",
    description="Weekly ICU sedation monitoring"
)

# Save a custom DAG pipeline
manage_pipeline(
  action="save",
    name="brca1_comprehensive",
    config="""
steps:
  - id: expand
    action: expand
    params: { topic: BRCA1 breast cancer }
  - id: pubmed
    action: search
    params: { query: BRCA1, sources: pubmed, limit: 50 }
  - id: expanded
    action: search
    inputs: [expand]
    params: { strategy: mesh, sources: pubmed,openalex, limit: 50 }
  - id: merged
    action: merge
    inputs: [pubmed, expanded]
    params: { method: rrf }
  - id: enriched
    action: metrics
    inputs: [merged]
output:
  limit: 30
  ranking: quality
"""
)

# Execute a saved pipeline
unified_search(pipeline="saved:icu_sedation_weekly")

# List & manage
manage_pipeline(action="list", tag="anesthesia")
manage_pipeline(action="load", source="brca1_comprehensive")  # Review YAML
manage_pipeline(action="history", name="icu_sedation_weekly")  # View past runs

🔍 Search Mode Comparison

┌─────────────────────────────────────────────────────────────────────────┐
│                        SEARCH MODE DECISION TREE                         │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                          │
│   "What kind of search do I need?"                                       │
│         │                                                                │
│         ├── Know exactly what to search?                                 │
│         │   └── unified_search(query="topic keywords")                   │
│         │       → Quick, auto-routing to best sources                    │
│         │                                                                │
│         ├── Have a clinical question (A vs B)?                           │
│         │   └── Agent P/I/C/O → parse_pico() handoff                  │
│         │       → unified_search(template:pico) or expanded Boolean    │
│         │                                                                │
│         ├── Need comprehensive systematic coverage?                      │
│         │   └── generate_search_queries() → parallel search              │
│         │       → MeSH expansion, multiple strategies, merge             │
│         │                                                                │
│         └── Exploring from a key paper?                                  │
│             └── find_related/citing/references → build_citation_tree     │
│                 → Citation network, research context                     │
│                                                                          │
└─────────────────────────────────────────────────────────────────────────┘

Mode	Entry Point	Best For	Auto-Features
Quick	`unified_search()`	Fast topic search	ICD→MeSH, multi-source, dedup
PICO	Agent P/I/C/O -> `parse_pico()`	Clinical questions	Validate handoff -> `template:pico` backend search
Systematic	`generate_search_queries()`	Literature reviews	MeSH expansion, synonyms
Exploration	`find_*_articles()`	From key paper	Citation network, related

🤖 Claude Skills (AI Agent Workflows)

Pre-built workflow guides in .claude/skills/, divided into Usage Skills (for using the MCP server) and Development Skills (for maintaining the project):

📚 Usage Skills (10) — For AI Agents Using This MCP Server

Skill	Description
`pubmed-quick-search`	Basic search with filters
`pubmed-systematic-search`	MeSH expansion, comprehensive
`pubmed-pico-search`	Clinical question decomposition
`pubmed-paper-exploration`	Citation tree, related articles
`pubmed-gene-drug-research`	Gene/PubChem/ClinVar
`pubmed-fulltext-access`	Europe PMC, CORE full text
`pubmed-export-citations`	RIS/BibTeX/CSV/CSL export guidance
`pubmed-multi-source-search`	Cross-database unified search
`pubmed-mcp-tools-reference`	Complete tool reference guide
`pipeline-persistence`	Save, load, reuse search plans

🔧 Development Skills (13) — For Project Contributors

Skill	Description
`changelog-updater`	Auto-update CHANGELOG.md
`code-refactor`	DDD architecture refactoring
`code-reviewer`	Code quality & security review
`ddd-architect`	DDD scaffold for new features
`git-doc-updater`	Sync docs before commits
`git-precommit`	Pre-commit workflow orchestration
`memory-checkpoint`	Save context to Memory Bank
`memory-updater`	Update Memory Bank files
`project-init`	Initialize new projects
`readme-i18n`	Multilingual README sync
`readme-updater`	Sync README with code changes
`roadmap-updater`	Update ROADMAP.md status
`test-generator`	Generate test suites

📁 Location: .claude/skills/*/SKILL.md (Claude Code-specific, and the single source of truth for repo skills) Do not mirror or split repo skills into .github/skills/. These repo skills are project-scoped and should remain version-controlled. Personal cross-project skills belong in a user directory such as ~/.copilot/skills/ or ~/.claude/skills/, not in this repository.

🏗️ Architecture (DDD)

This project uses Domain-Driven Design (DDD) architecture, with literature research domain knowledge as the core model.

src/pubmed_search/
├── domain/                     # Core business logic
│   └── entities/article.py     # UnifiedArticle, Author, etc.
├── application/                # Use cases
│   ├── search/                 # QueryAnalyzer, ResultAggregator
│   ├── export/                 # Citation export (RIS, BibTeX...)
│   └── session/                # SessionManager
├── infrastructure/             # External systems
│   ├── ncbi/                   # Entrez, iCite, Citation Exporter
│   ├── sources/                # Europe PMC, CORE, CrossRef...
│   └── http/                   # HTTP clients
├── presentation/               # User interfaces
│   ├── mcp_server/             # MCP tools, prompts, resources
│   │   └── tools/              # discovery, strategy, pico, export...
│   └── api/                    # Auxiliary HTTP API routes (not pubmed_search.api)
└── shared/                     # Cross-cutting concerns
    ├── exceptions.py           # Unified error handling
    └── async_utils.py          # Rate limiter, retry, circuit breaker

Internal Mechanisms (Transparent to Agent)

Mechanism	Description
Session	Auto-create, auto-switch
Cache	Auto-cache search results, avoid duplicate API calls
Rate Limit	Auto-comply with NCBI API limits (0.34s/0.1s)
MeSH Lookup	`generate_search_queries()` auto-queries NCBI MeSH database
ESpell	Auto spelling correction (`remifentanyl` → `remifentanil`)
Query Analysis	Each suggested query shows how PubMed actually interprets it

Vocabulary Translation Layer (Key Feature)

Our Core Value: We are the intelligent middleware between Agent and Search Engines, automatically handling vocabulary standardization so Agent doesn't need to know each database's terminology.

Different data sources use different controlled vocabulary systems. This server provides automatic conversion:

API / Database	Vocabulary System	Auto-Conversion
PubMed / NCBI	MeSH (Medical Subject Headings)	✅ Full support via `expand_with_mesh()`
ICD Codes	ICD-10-CM / ICD-9-CM	✅ Auto-detect & convert to MeSH
Europe PMC	Text-mined entities (Gene, Disease, Chemical)	✅ `get_text_mined_terms()` extraction
OpenAlex	OpenAlex Concepts (deprecated)	❌ Free-text only
Semantic Scholar	S2 Field of Study	❌ Free-text only
CORE	None	❌ Free-text only
CrossRef	None	❌ Free-text only

Automatic ICD → MeSH Conversion

When searching with ICD codes (e.g., I10 for Hypertension), unified_search() automatically:

Detects ICD-10/ICD-9 patterns via detect_and_expand_icd_codes()
Looks up corresponding MeSH terms from internal mapping (ICD10_TO_MESH, ICD9_TO_MESH)
Expands query with MeSH synonyms for comprehensive search

# Agent calls unified_search with clinical terminology
unified_search(query="I10 treatment outcomes")

# Server auto-expands to PubMed-compatible query
"(I10 OR Hypertension[MeSH]) treatment outcomes"

📖 Full architecture documentation: ARCHITECTURE.md

MeSH Auto-Expansion + Query Analysis

When calling generate_search_queries("remimazolam sedation"), internally it:

ESpell Correction - Fix spelling errors
MeSH Query - Entrez.esearch(db="mesh") to get standard vocabulary
Synonym Extraction - Get synonyms from MeSH Entry Terms
Query Analysis - Analyze how PubMed interprets each query

{
  "mesh_terms": [
    {
      "input": "remimazolam",
      "preferred": "remimazolam [Supplementary Concept]",
      "synonyms": ["CNS 7056", "ONO 2745"]
    }
  ],
  "all_synonyms": ["CNS 7056", "ONO 2745", ...],
  "suggested_queries": [
    {
      "id": "q1_title",
      "query": "(remimazolam sedation)[Title]",
      "purpose": "Exact title match - highest precision",
      "estimated_count": 8,
      "pubmed_translation": "\"remimazolam sedation\"[Title]"
    },
    {
      "id": "q3_and",
      "query": "(remimazolam AND sedation)",
      "purpose": "All keywords required",
      "estimated_count": 561,
      "pubmed_translation": "(\"remimazolam\"[Supplementary Concept] OR \"remimazolam\"[All Fields]) AND (\"sedate\"[All Fields] OR ...)"
    }
  ]
}

Value of Query Analysis: Agent thinks remimazolam AND sedation only searches these two words, but PubMed actually expands to Supplementary Concept + synonyms, results go from 8 to 561. This helps Agent understand the difference between intent and actual search.

🔒 HTTPS Deployment

Enable HTTPS secure communication for production environments.

Copilot Studio Quick Start

# Step 1: Generate SSL certificates
./scripts/generate-ssl-certs.sh

# Step 2: Start HTTPS service (Docker)
./scripts/start-https-docker.sh up

# Verify deployment
curl -k https://localhost/

HTTPS Endpoints

Service	URL	Description
MCP	`https://localhost/mcp`	Streamable HTTP MCP endpoint
Health	`https://localhost/health`	Health check
Info	`https://localhost/info`	Runtime transport and endpoint metadata
Exports	`https://localhost/exports`	Prepared export file listing

Remote MCP Client Configuration

{
  "mcpServers": {
    "pubmed-search": {
      "url": "https://localhost/mcp"
    }
  }
}

🏢 Microsoft Copilot Studio Integration

Integrate PubMed Search MCP with Microsoft 365 Copilot (Word, Teams, Outlook)!

Quick Start

# Start with Streamable HTTP transport (required by Copilot Studio)
pubmed-search-mcp-http --transport streamable-http --port 8765

# Enable Copilot-compatible HTTP semantics while keeping full tool schemas
pubmed-search-mcp-http --transport streamable-http --copilot-compatible --port 8765

# Or use the dedicated script with ngrok
./scripts/start-copilot-studio.sh --with-ngrok

Copilot Studio Configuration

Field	Value
Server name	`PubMed Search`
Server URL	`https://your-server.com/mcp`
Authentication	`None` (or API Key)

📖 Full documentation: copilot-studio/README.md

Use pubmed-search-mcp-http --copilot-compatible for packaged Copilot HTTP semantics. run_server.py remains a source-tree development wrapper; use run_copilot.py only when you need simplified tool schemas.

⚠️ Note: SSE transport deprecated since Aug 2025. Use streamable-http.

📖 More documentation:

Architecture → ARCHITECTURE.md

Pipeline tutorial (English) → docs/PIPELINE_MODE_TUTORIAL.en.md

Pipeline tutorial (zh-TW) → docs/PIPELINE_MODE_TUTORIAL.md

Deployment guide → DEPLOYMENT.md

Copilot Studio → copilot-studio/README.md

🔐 Security

Security Features

Layer	Feature	Description
HTTPS	TLS 1.2/1.3 encryption	All traffic encrypted via Nginx
Rate Limiting	30 req/s	Nginx level protection
Security Headers	XSS/CSRF protection	X-Frame-Options, X-Content-Type-Options
Streamable HTTP	`/mcp` endpoint	Modern MCP transport for remote clients
No Database	Stateless	No SQL injection risk
No Secrets	In-memory only	No credentials stored

See DEPLOYMENT.md for detailed deployment instructions.

📤 Export Formats

Export your search results in formats compatible with major reference managers:

Format	Source	Compatible With	Use Case
RIS	official or local	EndNote, Zotero, Mendeley	Universal import
MEDLINE	official or local	PubMed tools	Native PubMed-style archiving
CSL JSON	official	Citation processors	Programmatic citation styling
BibTeX	local	LaTeX, Overleaf, JabRef	Academic writing
CSV	local	Excel, Google Sheets	Data analysis
JSON	local	Programmatic access	Custom processing

Exported Fields

Core: PMID, Title, Authors, Journal, Year, Volume, Issue, Pages
Identifiers: DOI, PMC ID, ISSN
Content: Abstract (HTML tags cleaned)
Metadata: Language, Publication Type, Keywords
Access: DOI URL, PMC URL, Full-text availability

Special Character Handling

BibTeX exports use pylatexenc for proper LaTeX encoding
Nordic characters (ø, æ, å), umlauts (ü, ö, ä), and accents are correctly converted
Example: Søren Hansen → S{\o}ren Hansen

📚 Citation

GitHub will show Cite this repository from CITATION.cff. If you use PubMed Search MCP in research, methods sections, or internal technical reports, prefer the GitHub-generated citation or reuse the repository metadata directly.

@software{pubmed_search_mcp,
  title = {PubMed Search MCP},
  author = {u9401066},
  url = {https://github.com/u9401066/pubmed-search-mcp}
}

📄 License

Apache License 2.0 - see LICENSE

Name		Name	Last commit message	Last commit date
Latest commit History 285 Commits
.asset-aware-mcp		.asset-aware-mcp
.claude/skills		.claude/skills
.cline/skills		.cline/skills
.clinerules		.clinerules
.codex/skills		.codex/skills
.github		.github
.vscode		.vscode
copilot-studio		copilot-studio
data/pipeline_examples		data/pipeline_examples
docs		docs
memory-bank		memory-bank
nginx		nginx
scripts		scripts
src/pubmed_search		src/pubmed_search
tests		tests
.bandit		.bandit
.gitignore		.gitignore
.instruction_drift_fingerprint		.instruction_drift_fingerprint
.ngrok.env		.ngrok.env
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
ARCHITECTURE.md		ARCHITECTURE.md
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CONSTITUTION.md		CONSTITUTION.md
CONTRIBUTING.md		CONTRIBUTING.md
DEPLOYMENT.md		DEPLOYMENT.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
README.zh-TW.md		README.zh-TW.md
ROADMAP.md		ROADMAP.md
docker-compose.https.yml		docker-compose.https.yml
docker-compose.yml		docker-compose.yml
pyproject.toml		pyproject.toml
run_copilot.py		run_copilot.py
run_server.py		run_server.py
start.sh		start.sh
uv.lock		uv.lock
vulture_whitelist.py		vulture_whitelist.py

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