# NIH UMLS MCP Server

> Use this tool when you need to integrate medical terminology and coding systems into your AI applications, enabling searches, concept definitions, and code mappings. It solves problems related to medical data standardization, interoperability, and clinical decision support by providing access to the NIH UMLS and VSAC FHIR APIs. This server accepts API requests as input and returns standardized medical terminology and coding data as output, ideal for use cases involving healthcare data analysis, medical research, and clinical informatics.

Canonical page: https://skillsregistry.net/skills/feordin-nih-umls-mcp  
JSON: https://api.skillsregistry.net/v1/skills/feordin-nih-umls-mcp

## Description

An MCP server that provides access to the NIH UMLS API and the NIH VSAC FHIR API. This server enables AI models to search medical terminology, look up concept definitions, explore relationships between medical concepts, map codes between different medical coding systems, and work with curated clinical value sets.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** maps-location
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/oxh4u3eqtt)
- **Repository:** <https://github.com/feordin/nih-umls-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "feordin-nih-umls-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/feordin-nih-umls-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/feordin-nih-umls-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt