"A server for bio-medical data or research using MCP" matching MCP servers:

A tool that helps users conduct comprehensive research on complex topics by exploring questions in depth, finding relevant sources, and generating structured, well-cited research reports.

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

A bounded evidence review engine that ingests documents, extracts evidence for a given claim, detects contradictions, and produces auditable evidence packets without hallucinations or open-web research.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.

MCP server for technical research in the TOTVS Protheus ecosystem, enabling AI agents to query official documentation, community knowledge bases, and release notes through structured tools. It supports runtime error debugging and ADVPL code generation.

Provides access to OpenAlex's catalog of 240+ million scholarly works through 18 specialized tools for conducting literature reviews, analyzing citations, tracking research trends, and mapping the scholarly landscape including authors, institutions, and collaboration networks.

A remote MCP server for traceable research across JW.org and the Watchtower Online Library, providing tools for searching, retrieving articles, footnotes, cross-references, and daily texts with optional AI-powered synthesis.

A version-aware cache for web research that stores and serves documentation references, preventing models from re-researching the same topics across sessions.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Enables host assistants to discover and read the toolkit’s research methods over a local stdio MCP connection, supporting research planning, literature work, scientific reasoning, writing, figures, and academic document workflows. It does not itself search scholarly databases, generate research results, compile documents, or call models.

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

Provides access to research paper topics and lists via a remote SSE server, enabling users to explore folders, topics, and execute prompts.

Enables querying Polish data protection regulations, decisions, and guidelines from the UODO directly from AI assistants.

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Provides AI assistants with locally bundled tools to explore NIST OSCAL models, retrieve schemas, list community resources, and generate valid OSCAL templates for governance, risk, and compliance workflows.

Facilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables AI coding agents to triage BigQuery ML and BigQuery AI tasks, look up exact model and function syntax, and search 137 syntax cards, 14 reference guides, and 104 live-verified narratives via a zero-dependency stdio MCP interface.

A Model Context Protocol (MCP) server for Canvas Medical API/SDK development assistance, providing streamlined access to Canvas Medical documentation and AI-powered development tools.

Provides offline access to Ellen Gould Harmon's writings with full-text search, reference navigation, contextual retrieval, and PDF generation capabilities for academic research and study.

Enables AI clients to search live documentation for popular developer libraries and return clean, source-cited summaries via a RAG pipeline.

Extracts and stores documentation content from Microsoft Learn and GitHub URLs into PocketBase with full-text search, metadata preservation, and automatic collection management for easy retrieval and organization.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

Enables MCP clients to search, read, edit, fork, and create vaults and pages on doi.bio through its public HTTP API, with token-based writes and versioned page editing.

Enables offline, read-only question answering over a pinned ArduPilot, ExpressLRS, EdgeTX, CRSF, and MAVLink corpus with verifiable citations, plus parameter lookup, bitmask decoding, MAVLink message inspection, CRSF frame decoding, and Intel HEX checks.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

Serves read-only tools that let an agent inspect PDFs step by step: outline a document, search for text or figure values, read pages and tables in order, describe figures and chart data, and render page or block images, with every block cited to its page and bounding box. It also lists the PDFs available under a configured root folder, which it never reads outside of.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.