reactome-mcp

reactome-mcp

Enables coding agents to interact with the Reactome pathway database, including search, lookup, hierarchy traversal, SBML/SBGN export, and gene-set enrichment analysis.

Category
访问服务器

README

reactome-mcp

An MCP server that gives coding agents first-class access to the Reactome pathway database — search, look up, traverse, export to SBML/SBGN, and run gene-set pathway-enrichment analysis, all from the chat.

Python 3.12+ MCP License: MIT

Built with the official Python MCP SDK (FastMCP) over stdio. It wraps both Reactome REST services:

  • ContentService — full-text search, entity/pathway lookup, hierarchy traversal, participants, complex subunits, interactors, and SBML/SBGN/diagram export.
  • AnalysisService — submit a gene/protein list and get back ranked, enriched pathways with p-value/FDR.

Because it speaks plain MCP over stdio (no vendor-specific extensions), it works with any MCP-capable agent — Claude Code, Codex, Cursor, and others. Only the registration command differs.


Quickstart

Requires uv (which manages Python ≥ 3.12 for you).

git clone https://github.com/tc2fh/reactome-mcp.git
cd reactome-mcp
uv sync                     # install runtime deps
uv run reactome-mcp         # boots the server on stdio (Ctrl+C to exit)

Then register it with your agent (see below) and ask it something like:

"Run Reactome enrichment on TP53, EGFR, BRCA1, MDM2, CDKN1A and list the 5 most significant pathways with their FDR."


Register with your agent

The server is a standard stdio MCP server launched with uv run reactome-mcp. Run these from inside the cloned directory.

Claude Code

claude mcp add reactome -- uv --directory "$PWD" run reactome-mcp

Codex

codex mcp add reactome -- uv --directory "$PWD" run reactome-mcp

…or add a table to ~/.codex/config.toml:

[mcp_servers.reactome]
command = "uv"
args = ["run", "--directory", "/absolute/path/to/reactome-mcp", "reactome-mcp"]

Cursor — add to .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):

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

Any other MCP client (Windsurf, Cline, Zed, Pi, …) — point it at the same stdio command. A ready-to-edit example lives in .mcp.json:

{
  "mcpServers": {
    "reactome": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/reactome-mcp", "reactome-mcp"],
      "env": {}
    }
  }
}

Tools

All tools are async, return JSON-shaped dicts (or a path string for downloads), and degrade to {"error": ...} rather than raising on HTTP/network failures.

Search & lookup

Tool Signature Purpose
search (query, species=None, types=None, rows=10, start=0, cluster=True) Solr free-text search; returns flattened, highlight-stripped entries + per-type counts.
get_entry (stable_id, enhanced=False, attribute=None, max_chars=50000) Full record for any object by stable id/dbId; size-guarded; attribute fetches one field.
get_entries (stable_ids) Batch lookup for up to 20 identifiers at once.

Pathway hierarchy & participants

Tool Signature Purpose
list_top_level_pathways (species) Top-level pathways (browser entry points) for a species name/taxId.
list_pathway_events (stable_id) All sub-pathways and reactions contained in a pathway (recursive).
get_event_ancestors (stable_id) Paths from an event up to its top-level pathway(s) — breadcrumbs.
get_event_participants (stable_id) Physical entities (+ their reference entities) in a reaction/pathway.
find_pathways_for_entity (stable_id, species=None, all_forms=False) Which lower-level pathways contain a given molecule/complex.

Entities, interactors, reference data

Tool Signature Purpose
get_complex_subunits (stable_id, exclude_structures=False) Recursively list the subunits of a complex.
get_interactors (accession, page=-1, page_size=-1) Curated IntAct protein–protein interactors for an accession.
list_species (main_only=True) Species annotated in Reactome (name, taxId, abbreviation).
list_diseases () Diseases (Disease Ontology terms) annotated in Reactome.

Exporters

Tool Signature Purpose
get_event_sbml (stable_id, fmt="sbml", max_chars=50000) Export a pathway/reaction to SBML or SBGN inline; head/tail truncation past max_chars.
download_export (stable_id, kind="event", ext="sbml", save_dir="./reactome_downloads") Stream an export to disk (event→sbml/sbgn, diagram/reaction/fireworks→png/svg, document→pdf).

Enrichment analysis

Tool Signature Purpose
analyze_identifiers (identifiers, projection=True, species=None, sort_by="ENTITIES_PVALUE", p_value=1.0, page_size=20, page=1, include_interactors=False) Submit a gene/protein list; returns a token + ranked enriched pathways with pValue/FDR.
get_analysis_results (token, species=None, sort_by="ENTITIES_PVALUE", p_value=1.0, page=1, page_size=20, resource="TOTAL") Page/sort/filter a prior analysis by its token (no re-submission).
get_analysis_not_found (token, page=0, page_size=40) Identifiers from the submission that did not map to Reactome.

Design notes

  • Reactome stable ids look like R-HSA-69278; numeric dbIds and plain accessions (e.g. P04637) are also accepted. Path identifiers are validated so they can't escape the intended endpoint.
  • search strips Reactome's Solr <span class="highlighting"> markup and flattens the clustered results[] → entries[] shape into one list.
  • get_entry and get_event_sbml are size-guarded so multi-MB records / SBML never flood the chat — they point you to download_export, which streams to disk.
  • analyze_identifiers returns a token; reuse it with get_analysis_results / get_analysis_not_found to page and inspect without re-running the analysis.

Example prompts

  1. Search + lookup"Search Reactome for TP53, then look up 'Transcriptional Regulation by TP53' and summarise what it does."
  2. Enrichment"Run Reactome enrichment on TP53, EGFR, BRCA1, MDM2, CDKN1A and list the 5 most significant pathways with their FDR."
  3. SBML export"Find 'Cell Cycle Checkpoints', export its SBML to ./reactome_downloads, and tell me how many species and reactions it defines."
  4. Hierarchy"List the top-level human pathways, then drill into 'Cell Cycle' and show its contained events."

Development

uv sync --extra dev     # install test deps (pytest, pytest-asyncio, respx)
uv run pytest           # offline suite — every request is mocked via respx

The suite (tests/) runs entirely offline against captured fixtures in samples/, so it needs no network. Smoke-test the live server in any of these ways:

uv run reactome-mcp                          # console script
uv run python -m reactome_mcp                # module entry point
uv run python server.py                      # source-checkout shim
uv run mcp dev src/reactome_mcp/server.py    # MCP Inspector dev UI
reactome-mcp/
├── src/reactome_mcp/   # installable package (server.py = all tools + helpers)
├── server.py           # source-checkout compatibility shim
├── tests/              # offline pytest suite (respx-mocked)
├── samples/            # captured API responses used as fixtures
├── pyproject.toml      # uv-managed project
└── .mcp.json           # example stdio MCP config

Acknowledgements

Powered by Reactome, a free, open-source, open-access, curated and peer-reviewed pathway database. Please cite Reactome when publishing work that uses this data — see https://reactome.org/cite.

This project is not affiliated with or endorsed by the Reactome team.

License

MIT © Tien Comlekoglu

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选