"Information about medical law" matching MCP servers:

Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.

Provides medical evidence tools including PubMed search, citation verification, clinical trial lookup, drug information, drug interaction checking, PICO extraction, and medical entity recognition via MCP.

Provides medical symptom extraction, differential diagnosis generation, PubMed literature search, and abstract summarization tools for LLM agents.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

Enables natural language interaction with the Korean Personal Information Protection Act (PIPA) through 37 MCP tools that search, compare, analyze, and verify legal texts, official guidelines, and consulting cases from authoritative sources.

Query 37 EU regulations — from GDPR and AI Act to DORA, MiFID II, eIDAS, Medical Device Regulation, and more — directly from Claude, Cursor, or any MCP-compatible client.

A Model Context Protocol server that lets Claude (and any MCP client) cite actual Canadian financial-services regulations: OSFI, PIPEDA, FINTRAC, Quebec Law 25.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables intelligent search and retrieval of Japanese legal statutes through the e-Gov API. Supports smart lookup of laws and articles with abbreviation recognition, batch processing, and multi-tier caching for high-performance legal research.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

GraphRAG MCP server for querying SNOM CT medical terminology via Neo4j, offering graph_search_concept and graph_get_related tools to search and traverse concepts and relationships.

Enables AI agents to precisely retrieve and analyze PDF assets (tables, figures, sections) via MCP, with knowledge graph integration for medical research.

MCP server for searching French and European law, providing natural language access to jurisprudence, consolidated legal texts, and related legal information via tools like search_decisions and search_legal_texts.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.