"A resource for finding medical information" matching MCP servers:

An MCP server that provides comprehensive medical information by querying multiple authoritative medical APIs including FDA, WHO, PubMed, Google Scholar, and RxNorm.

This MCP server provides authoritative medical data from FDA, WHO, PubMed, RxNorm, and Google Scholar, enabling drug information lookup, health statistics retrieval, medical literature searching, and clinical guidelines access directly in AI workflows.

Enables AI tools to locally query authoritative medical sources like FDA, WHO, PubMed, RxNorm, and related APIs for drug information, health statistics, clinical guidelines, trials, and medical literature without API keys.

Enables AI clients to query authoritative medical sources such as FDA, WHO, PubMed, RxNorm, and ClinicalTrials.gov for drug information, safety reports, health statistics, guidelines, pediatric references, and evidence-graded literature. Runs locally with no API keys required, and adds resilience tooling like health checks, caching, and rate-limit-aware fallbacks.

Enables AI environments to query authoritative medical APIs for drug information, literature, health statistics, clinical guidelines, trials, and pediatric sources locally without API keys.

A local MCP server for read-only querying of AWS resources (Lambda, S3) via Boto3, currently exposing a health check tool with planned tools for listing resources and checking free tier.

Enables AI workflows to query authoritative medical APIs locally without API keys, covering drug information, health statistics, medical literature, clinical guidelines, clinical trials, and pediatric sources. Provides resilient access with caching, health checks, and evidence-graded results.

Enables AI assistants to query authoritative sources such as FDA, WHO, PubMed, RxNorm, and ClinicalTrials.gov for drug information, health statistics, medical literature, pediatric guidance, and clinical guidelines. Runs locally with no API keys required and includes resilience features like caching, rate limiting, circuit breakers, and health checks.

Enables AI assistants to query authoritative medical sources such as FDA, WHO, PubMed, RxNorm, and ClinicalTrials.gov from a local, key-free server. Provides drug searches, health statistics, graded medical literature, clinical guidelines, pediatric resources, and built-in health monitoring without sending data to the cloud.

Enables AI clients to query authoritative medical APIs locally without API keys for drug information, health statistics, medical literature, clinical guidelines, and pediatric sources.

Enables ChatGPT to inspect public Instagram, Threads, and web resources through a local browser and return a schema-validated Evidence Packet for evidence-first review.

Enables AI assistants and MCP clients to query authoritative medical APIs for drug information, health statistics, medical literature, clinical guidelines, clinical trials, and pediatric resources. Runs locally without API keys, providing private access to sources such as FDA, WHO, PubMed, RxNorm, and Google Scholar.

Enables AI clients to query FDA, WHO, PubMed, RxNorm, ClinicalTrials.gov, and other authoritative medical sources for drug information, safety data, health statistics, clinical guidelines, trials, and pediatric literature. Runs entirely locally with no API keys, adding resilience features like rate limiting, circuit breakers, caching, evidence grading, and health monitoring.

Enables AI assistants to query authoritative sources like FDA, WHO, PubMed, RxNorm, and Google Scholar for drug information, health statistics, clinical guidelines, trials, and medical literature. It runs locally with no API keys, so data stays private while results come back with evidence grading and resilient fallbacks.

Enables AI assistants to query authoritative medical sources such as FDA, WHO, PubMed, RxNorm, and ClinicalTrials.gov for drug information, safety data, health statistics, clinical guidelines, pediatric resources, and evidence-graded literature. Runs locally with no API keys or data leaving the machine.

An MCP server that provides comprehensive medical information by querying authoritative APIs including FDA, WHO, PubMed, RxNorm, and Australia's PBS, enabling drug searches, health statistics, medical literature, drug nomenclature, and PBS subsidy queries.

MCP server for connecting Claude Desktop to a local medical knowledge base with real-time file watching, enabling natural language interaction with medical documents.

Enables AI assistants to look up medical billing codes, denial reasons, and payer rules for faster claim resolution.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

A Model Context Protocol server for querying and analyzing Azure resources at scale using Azure Resource Graph, enabling AI assistants to explore and monitor Azure infrastructure.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

A collection of MCP servers providing medical calculation tools (eGFR, pregnancy due dates, pediatric asthma score, strep McIsaac) for AI assistants, with support for Chinese medical terminology and detailed clinical explanations.

Local MCP server for A-share stock trading via Tonghuashun, offering account/position queries, buy/sell/cancel orders with risk controls and forced user confirmation; currently simulated with a reserved interface for real broker channels.

An MCP server for searching and downloading books from Library Genesis, supporting EPUB, MOBI, PDF, and more through natural language queries.

Provides a local MCP server for AI-powered web crawling on Windows, enabling agents to directly crawl and extract web content without requiring WSL.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables LLM clients to access crop yield predictions from RGB imagery, topic-based PDF literature, and soil lab/moisture CSV data.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.