"A system for legal document analysis and contract management" matching MCP servers:

Enables querying a style contract, resolving applicable rules for a given task/evidence/consequence, and inspecting recorded sample cases through MCP tools.

This MCP server enables reading and analyzing various file formats, including PDF, DOCX, XLSX, and CSV, by extracting metadata, searching for keywords, and reading structured content. It also provides capabilities to create new documents and convert files between supported formats.

Enables legal document analysis using Azure Blob Storage and Anthropic Claude API with streaming capabilities, prompt library management, and document retrieval for PDF files.

Enables extracting explicitly stated contract metadata, clauses, and obligations from user-supplied contract text without storing or modifying the data.

A stateless MCP server for composing, validating, auditing, and rendering consequential documents from a small semantic model.

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 MCP clients to extract text from PDF, image, Word, PowerPoint, and Excel files using MinerU, either on the local machine or a self-hosted private-network API. Parsed results are saved locally as Markdown, structured JSON, and extracted images, which can then be read back in segments.

Enables AI agents to efficiently manage and update component documentation with precise partial updates, saving up to 75% tokens compared to full rewrites.

Enables coding agents to check requests against a live OpenAPI 3.x specification rather than guessing, by resolving endpoints from a docs anchor, operationId, path fragment or tag, revealing what each operation accepts and returns with $refs resolved, and diffing a concrete payload to report missing, unknown, mistyped, or forbidden fields with suggested corrections. All five tools are strictly read-only, never touching the API the schema describes, and return actionable errors with close-match suggestions.

A no-server MCP toolkit that organizes project wikis from Feishu documents, messages, and more, enabling AI clients to answer questions with cited evidence to prevent hallucinations.

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.

Provides legal procedure guidance, document templates, and calculators for labor disputes such as wage arrears and unfair dismissal.

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.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Provides access to microCMS documentation, enabling AI assistants to search and retrieve the latest document content.

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

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

MCP server for managing product documentation as markdown files. Supports creating, reading, updating, deleting, and searching docs with optional semantic search.

Private local stdio MCP server for metadata-only discovery, recording, and querying of Markdown and PDF documents.

Enables clients to inspect local PDFs by searching text, rendering pages, and cropping selected regions at high resolution directly from the source document. It runs locally over stdio without requiring models, embeddings, GPUs, or API keys, and does not modify source PDFs or send network requests.

Enables AI clients to validate OpenAPI specs, compare versions, detect breaking changes, and generate changelogs.

Provides AI tools with access to ProudNet documentation through search, page retrieval, and section listing capabilities. Enables users to query ProudNet networking library documentation using natural language.

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

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.

A sophisticated server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

Local document intelligence for AI agents — extract text, detect tables, read metadata, analyze structure, search keywords, and detect language from PDF and DOCX files. No cloud API required, no API key needed.

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

Enables AI assistants to search and retrieve content from the Chinese Minecraft Wiki, including article introductions, full content, and category lists.

Provides tools to authenticate, search, and manage sessions within WingArc's iA Document Management System. It enables users to perform free word document searches and manage security tokens through the Model Context Protocol.

A Model Context Protocol (MCP) server that allows interaction with PDF and EPUB documents, designed to work with Windsurf IDE by Codeium.