deploying-openmed-mcp
Run OpenMed's Model Context Protocol (MCP) server so coding agents (Claude Code, Codex) and chat clients can call clinical NER, PII extraction, and de-identification as tools, on-device. Use when the user wants to add OpenMed to an agent's MCP config, expose de-id/NER as MCP tools, run an MCP server over stdio or Streamable HTTP, give Claude/Codex access to OpenMed, or containerize the MCP server. Covers the mcp extra, create_mcp_server, the 7 tools (openmed_analyze_text, openmed_extract_pii, openmed_deidentify, openmed_list_models, openmed_list_pii_languages, openmed_loaded_models, openmed_un
npx skills add maziyarpanahi/openmed --skill deploying-openmed-mcp --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Deploying the OpenMed MCP server `openmed.mcp.server` exposes OpenMed's clinical NLP as **Model Context Protocol** tools, so coding agents (Claude Code, Codex) and chat clients can de-identify and analyze clinical text by calling tools instead of writing glue code. It runs **on-device** — models are local, no telemetry — and the server instructs clients to send real PHI only to instances the user operates. ## When to use this skill When an agent or LLM client should be able to *invoke* OpenMed: add it to a coding agent's MCP config, give a chat client de-id/NER tools, or run a shared MCP endpoint for a team. For programmatic HTTP from your own services, prefer `serving-openmed-rest-api`; for corpora, `batch-processing-clinical-text`. ## Quick start ```bash pip install "openmed[mcp]" # FastMCP / MCP SDK # stdio transport (what coding agents spawn): default python -m openmed.mcp.server # Streamable HTTP transport (network-reachable): python -m openmed.mcp.server --transport streamable-http --host 127.0.0.1 --port 8081 ``` ```python # Or embed it: from openmed.mcp.server import create_mcp_server server = create_mcp_server() # FastMCP("OpenMed", ...) with tools+resources+prompts serv
- When to use this skill
- Quick start
- The 7 tools (confirmed in openmed/mcp/server.py)
- Adding it to a coding agent
- Runtime config
- Running in Docker
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
pip install "openmed[mcp]" # FastMCP / MCP SDK stdio transport (what coding agents spawn): default python -m openmed.mcp.server Streamable HTTP transport (network-reachable): python -m openmed.mcp.server --transport streamable-http --host 127.0.0.1 --port 8081
What does the deploying-openmed-mcp skill do?
Run OpenMed's Model Context Protocol (MCP) server so coding agents (Claude Code, Codex) and chat clients can call clinical NER, PII extraction, and de-identification as tools, on-device. Use when the user wants to add OpenMed to an agent's MCP config, expose de-id/NER as MCP tools, run an MCP server over stdio or Streamable HTTP, give Claude/Codex access to OpenMed, or containerize the MCP server. Covers the mcp extra, create_mcp_server, the 7 tools (openmed_analyze_text, openmed_extract_pii, openmed_deidentify, openmed_list_models, openmed_list_pii_languages, openmed_loaded_models, openmed_un
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill deploying-openmed-mcp --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.