# Cardiology Knowledge Graph MCP

> Use this tool when you need to extract medical entities and relationships from cardiology documents and perform natural language queries to gain clinical insights. It solves problems related to information retrieval and knowledge management in cardiology by building and managing a specialized knowledge graph. The tool takes in PDF documents and outputs refined data and query results, enabling users to refine extracted data and perform queries to inform clinical decision-making.

Canonical page: https://skillsregistry.net/skills/ryoureddy-cardiology-knowledge-graph-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ryoureddy-cardiology-knowledge-graph-mcp

## Description

Builds and manages a cardiology-focused knowledge graph in Neo4j by extracting medical entities and relationships from documents using LLMs. It enables users to ingest PDFs, refine extracted data, and perform natural language queries to gain clinical insights.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/pj6lg2hqji)
- **Repository:** <https://github.com/ryoureddy/Cardiology-Knowledge-Graph-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": "ryoureddy-cardiology-knowledge-graph-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ryoureddy-cardiology-knowledge-graph-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ryoureddy-cardiology-knowledge-graph-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