"Guidance for Writing a Complex Document and Understanding Legal Requirements" matching MCP servers:

Enables searching and retrieving NICE guidance for England — technology appraisals, clinical guidelines, diagnostics, interventional procedures, and quality standards — with full recommendation text, status, and supersession details.

Enables any MCP client to search and read 127 FDA guidance documents on clinical research, with tools for ranked passage search, document retrieval, and metadata lookup.

Enables searching a manually curated database of stable macromolecular protein complexes by protein or complex name, gene, GO term, or biological process, and fetching individual records by accession to retrieve subunits with UniProt identifiers, biological roles, and stoichiometry. Complements UniProt, IntAct, and STRING, and can be used keyless over a hosted gateway endpoint or run locally over stdio.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

An MCP server for Indian legal work that enables case-law research with verified citations, limitation and deadline calculations, legal document drafting, matter tracking, and contract search.

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables parsing and extraction of content from various document formats (PDF, Word, Excel, PowerPoint) into Markdown format using the Niutrans document API.

Enables deterministic research of Korean tax-law issues by retrieving and scoring official statutory evidence from law.go.kr and other legal sources, with citation verification and provenance, without deciding legal conclusions.

Hey @roocode community! I'm thrilled to share a project born from my work with Roocode and the vision of an AI-powered development team: the Anubis MCP Server! This system is heavily inspired by Roocode and designed to orchestrate an AI development workflow based on agile methodology. It simulates

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

An MCP server that uses Google's Gemini API to analyze videos and convert them to text descriptions that Claude Code can understand and act upon.

Provides AI agents with comprehensive document parsing capabilities including PDF text extraction, OCR, HTML-to-markdown conversion, table extraction, and summarization, optimized for agent workflows.

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.

Cite-grade German legal-text infrastructure for LLM agents, providing access to federal, Länder, and EU laws with cryptographic provenance.

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.

Enables law knowledge Q&A with traceable, citation-verified answers from a shared 38-law article-level index, plus single and batch police incident risk assessment grounded in retrieved statutes. Exposed as stdio MCP tools (law_qa, risk_assess, risk_assess_batch) for dsh, Claude Desktop, Cline, and other MCP clients.

Enables local, on-device legal document analysis including clause extraction, risk flagging, compliance checking, and clause-level version comparison without sending documents externally.

MCP server for AI-assisted PDF reading with pagination, search, and page navigation.

Enables AI assistants and agents to search a legal document collection via hybrid BM25+dense retrieval, read articles with context, and generate full or short citations. It also exposes whole acts as Markdown resources, provides a citation-answering prompt, and supports stdio and streamable HTTP transports.

Enables intelligent document processing by extracting text, classifying document types, and generating structured summaries from PDFs and images using vision LLMs.

A production-ready template for developing Model Context Protocol (MCP) servers using Python and FastMCP.

A secure Model Context Protocol server that enables Claude AI to list, search, and read local documents in formats such as PDF, DOCX, and XLSX. It features read-only access with path traversal protection and supports deployment via Docker with SSE or STDIO transport options.

An MCP server that uses the Docling toolkit to convert various document formats, including PDFs, Office files, images, and audio, into clean Markdown for AI processing. It supports multiple processing pipelines like VLM and ASR with intelligent auto-detection and job queue management.

Efficiently delivers project documentation to AI agents like Claude on-demand, optimizing token usage by loading context only when needed. Supports document retrieval, listing, and keyword search with security features.

A comprehensive document analysis server that performs sentiment analysis, keyword extraction, readability scoring, and text statistics while providing document management capabilities including storage, search, and organization.

Run free live audience Q&A and polls from your agent: start an AskOne room, add and launch a poll, approve or hide questions and mark them answered, close the room, then read the questions as a FAQ draft and get the results. Free AI moderation. Uses an organization API token from askone.org

Checks documents against international standards (e.g., ICAO, Hague-Visby) to verify internal consistency and completeness, returning a machine-readable verdict for agent decision-making.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.