medeval-mcp
Enables MCP clients to operate MedEval regulatory workspaces, including evidence retrieval, generation, evaluation, and immutable document revisions, all through a local, secure gateway.
README
MedEval MCP (local)
Local-only Model Context Protocol gateway for the private MedEval medical-device regulatory workbench.
It lets an MCP client operate MedEval workspaces, canonicalized materials, evidence retrieval, CEP/CER generation and evaluation, workflow observability, and immutable document revisions. MedEval remains the system of record; this project is a thin, bounded adapter.
Safety boundary
- stdio transport only; no network MCP listener
- connects to loopback by default, with explicit opt-in for trusted private-network IPs
- explicit workspace/task identifiers
- allowlisted local upload roots
- no delete, shell, deployment, or secret-management tools
- asynchronous starts return task IDs and must be polled, not duplicated
- product facts cannot be derived from external literature
- document edits require the current
base_revision_id
See SECURITY.md.
Requirements
- Windows or another Python 3.11+ environment
- MedEval running at
http://127.0.0.1:8000 - Python package dependencies from
pyproject.toml
Install
cd C:\path\to\medeval-mcp
py -3.11 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -e ".[test]"
Copy-Item .env.example .env
The MCP process reads normal environment variables. MCP clients generally supply them in their server configuration rather than loading .env automatically.
Configure
Minimum local configuration:
MEDEVAL_BASE_URL=http://127.0.0.1:8000
MEDEVAL_PROJECT_ROOT=C:\path\to\medeval
MEDEVAL_ALLOWED_FILE_ROOTS=C:\path\to\medeval;C:\path\to\materials
If MedEval enables MEDEVAL_API_TOKEN, give this MCP process the same MEDEVAL_API_TOKEN; it is sent as X-API-Token. A signed browser/session token can instead be supplied as MEDEVAL_BEARER_TOKEN.
macOS client to a trusted LAN backend
When MedEval is listening on another computer in the same trusted LAN, use its literal private IP and opt in explicitly:
MEDEVAL_BASE_URL=http://192.168.0.166:8001
MEDEVAL_ALLOW_PRIVATE_NETWORK=true
MEDEVAL_PROJECT_ROOT=/Users/your-name/MedEval
MEDEVAL_ALLOWED_FILE_ROOTS=/Users/your-name/MedEval:/Users/your-name/Documents
The MCP remains a local stdio process on the Mac. Public IP addresses and domain names are still rejected.
Connect from Codex
Use examples/codex.mcp.json as the MCP server definition. The command must point to this project's .venv Python and use:
-m medeval_mcp.server
Restart Codex after changing the MCP configuration, then ask it to call medeval_health.
Run manually
MCP stdio uses standard output for protocol messages, so a manual run appears idle:
.\.venv\Scripts\python.exe -m medeval_mcp.server
Use an MCP client or Inspector to interact with it.
The bundled local launcher sets the loopback backend and upload roots before starting stdio:
.\scripts\run_local.ps1
Protocol and backend smoke test:
.\.venv\Scripts\python.exe scripts\mcp_smoke.py --health
Tool surface
The first release provides 21 tools:
- workspace: health, project list, create/get workspace, workspace documents
- observability: run list, run detail, bounded agent context
- evidence: project documents, evidence search, section context
- ingestion: allowlisted local material upload and canonicalization
- generation: template list, start generation, generation/task status
- evaluation: start evaluation
- revisions: list, bounded read, diff, optimistic-lock patch
medeval_start_generation defaults to tag_index_agent, which uses MedEval's dependency-serial segmented writer. Full-context strategies remain opt-in.
Tests
.\.venv\Scripts\python.exe -m pytest
The tests use mock HTTP transports and do not start LLM generation or consume model quota. A separate integration smoke test lists MCP tools and calls the already-running local /api/health endpoint.
Not included yet
- remote Streamable HTTP transport
xiaoyuu.medeployment- multi-user API keys and workspace ownership
- remote upload staging
- destructive project/task deletion
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。