Clinicaltrials

Conversational access to 400,000+ clinical trials — for pharma analysts, CRO teams, and health-tech builders.

A Python MCP server that connects Cursor, Claude Desktop, and any MCP-compatible client directly to ClinicalTrials.gov. Ask questions in plain English. Get clean, structured answers in seconds — no API keys, no setup, no parsing raw JSON.

PyPI version Python 3.12+ License: MIT

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The Problem

ClinicalTrials.gov is the world's largest registry of clinical research — 400,000+ trials, decades of outcomes data, all publicly accessible via a free API.

But the API gives you this:

{
  "protocolSection": {
    "eligibilityModule": {
      "eligibilityCriteria": "Inclusion Criteria:\n\n* Willing to sign the informed consent form;\n* ≥18 years old;\n* Diagnosed histologically or cytologically with local advanced or metastatic HER2-positive malignant solid cancer...",
      "stdAges": ["ADULT", "OLDER_ADULT"]
    },
    "adverseEventsModule": {
      "eventGroups": [...],
      "seriousEvents": [...],
      "otherEvents": [...]
    }
  }
}

Every field is optional. Eligibility criteria are a single wall of free text. Adverse events are split across two arrays that reference a separate groups table by ID. Every trial returns a different shape.

To compare eligibility across 10 trials, you write 10 scripts — or spend two days copying into Excel. To find the adverse event profile of a completed study, you navigate five nested objects and cross-reference three tables. To do a competitive landscape analysis across a therapeutic area, you're looking at a week of work.

This is not a data problem. It is a tooling problem.

With clinicaltrials-mcp, the same work takes a conversation:

"Compare the eligibility criteria for these 5 NASH trials" "What were the Grade 3+ adverse events in NCT02142803?" "Find recruiting HER2-positive trials near Mumbai"

Seconds. Followed up. Drilled down. No scripts, no parsing, no Excel.

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Install & Setup

Step 1 — Install

pip install clinicaltrials-mcp

No API key. No account. No configuration required.

Step 2 — Add to your MCP client

{
  "mcpServers": {
    "clinicaltrials": {
      "command": "uvx",
      "args": ["clinicaltrials-mcp"]
    }
  }
}

uvx runs the package without requiring it on your $PATH. If you don't have uv: pip install uv.

Restart your MCP client. The 5 tools appear automatically.

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Running from source (contributors / developers)

git clone https://github.com/agents100x/clinicaltrials-mcp
cd clinicaltrials-mcp
uv sync

{
  "mcpServers": {
    "clinicaltrials": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/clinicaltrials-mcp",
        "clinicaltrials-mcp"
      ]
    }
  }
}

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Where is the config file?

Client	Config file location
Claude Desktop (macOS)	~/Library/Application Support/Claude/claude_desktop_config.json
Claude Desktop (Windows)	%APPDATA%\Claude\claude_desktop_config.json
Cursor	.cursor/mcp.json or Settings → MCP
VS Code / forks	Settings → MCP Servers → Edit config

Restart the client after saving. The 5 tools appear automatically.

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Sample Queries

Organised by real analyst workflows. All queries work in plain English — no syntax required.

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Competitive Intelligence

"Show me recruiting Phase III trials for breast cancer"
"Find all active Phase II Alzheimer's trials in Europe"
"What NASH trials completed in the last 2 years?"
"Are there any recruiting trials for KRAS-mutant lung cancer?"
"Show me TERMINATED trials for diabetes — I want to know what failed"

The last query uses the status override: "...status='TERMINATED'" — WITHDRAWN and TERMINATED trials are excluded by default, but you can always ask for them explicitly.

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Patient Eligibility & Site Feasibility (CRO Teams)

"Get the full eligibility criteria for NCT04280341"
"Find recruiting HER2-positive breast cancer trials near Mumbai, India"
"Find Type 2 Diabetes trials recruiting near Bangalore, India"
"Are there any recruiting immunotherapy trials near London?"

Note on site status: Results are filtered by overall trial status. Always verify the specific site's recruitment status at clinicaltrials.gov before contacting a site.

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Protocol Comparison (Medical Writers / Researchers)

"Compare NCT04280341 and NCT02142803 on eligibility criteria"
"Compare the design and timeline of NCT04280341 and NCT03817944"
"Compare the endpoints of these three pembrolizumab trials: NCT04280341, NCT03817944, NCT02142803"

Focus the comparison with compare_on:

• "...compare on design" — phase, randomisation, blinding, arms
• "...compare on eligibility" — inclusion/exclusion criteria side by side
• "...compare on endpoints" — primary and secondary outcomes
• "...compare on timeline" — start dates, completion dates, enrollment targets
• "...compare on locations" — countries and site counts

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Results & Safety Review

"What were the adverse events reported in NCT02142803?"
"Show me all Grade 1 and above adverse events for NCT02142803"
"Give me a quick summary of the primary outcome of NCT02142803"
"What were the participant flow and dropout reasons in NCT02142803?"

Results options:

• Default shows serious adverse events only (Grade 3+)
• "...include all grades" → adds Grade 1/2 events
• "...summary only" → returns just the primary outcome in 2–3 lines, useful for scanning many trials

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Edge Cases the Tool Handles Cleanly

"Get results for NCT03444521"
→ Returns: "Trial completed in 2022 but no results posted — this is common,
   ~60% of completed trials never post results. It does not mean the trial failed."

"Find recruiting trials for Niemann-Pick disease near Paris"
→ Returns: helpful no-results message with suggestions to broaden the search

"Find terminated trials for a condition"
→ Requires explicit status override — the tool tells you exactly how

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The 5 Tools

Tool	What It Does	Key Design Decision
search_trials	Search by condition, phase, status, location	WITHDRAWN + TERMINATED excluded by default — always explained, always overridable
get_trial_details	Full structured summary by NCT ID	Eligibility always verbatim — never paraphrased
compare_trials	Side-by-side comparison of 2–5 trials	compare_on param: all, design, eligibility, endpoints, timeline, locations
get_trial_results	Outcomes, AEs, participant flow	AE grade note top + bottom; 4-case no-results handling
find_recruiting_near	Recruiting trials near a location	City → country location rings, honest about CT.gov's city-level data

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Why This Is Different

Every other ClinicalTrials.gov MCP passes raw JSON from the API to the LLM and calls it done.

This is a workflow tool, not an API wrapper:

• Outputs designed around what analysts do — not what the API can return
• Opinionated defaults — RECRUITING sorts first, Grade 3+ AEs shown, status filter always explained
• Honest about data gaps — ~60% of completed trials never post results; we say so every time
• Verbatim eligibility criteria — never paraphrased, too risky to interpret clinical text
• Verification by design — NCT hyperlinks appear at the top and bottom of every trial detail

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Limitations

• Data is self-reported by sponsors on ClinicalTrials.gov — not independently verified by us
• ~60% of completed trials have no posted results on CT.gov
• Trial status and site data may lag real-world updates by weeks
• Location filtering is city/country level — no GPS radius search available
• Not all trials in the world are registered on CT.gov

Data returned by this tool should never be used directly in regulatory submissions, publications, or clinical decisions without independent verification at clinicaltrials.gov.

Full scope and data disclaimer: DISCLAIMER.md

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How It Works

Claude or Cursor
      |
      | MCP tool call over stdio
      |
  server.py      — registers 5 tools, routes calls, catches all errors gracefully
      |
  client.py      — async httpx client for CT.gov API v2 (public, no auth required)
      |
  ClinicalTrials.gov  — public REST API, 400,000+ registered trials
      |
  formatters.py  — transforms raw JSON into clean, structured markdown
      |
Claude or Cursor — reads formatted output and responds to the user

Stack: Python 3.12+ · MCP Python SDK · httpx · uv

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Contributing

git clone https://github.com/agents100x/clinicaltrials-mcp
cd clinicaltrials-mcp
uv sync
uv run pytest tests/ -m "not integration"   # offline unit tests, ~0.3s
uv run pytest tests/                         # full suite including live API

Bug reports and pull requests welcome at github.com/agents100x/clinicaltrials-mcp/issues.

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License

MIT — see LICENSE for details.

Built by agents100x.