"A service for document processing" matching MCP servers:

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.

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.

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.

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.

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.

A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.

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.

Enables AI agents to retrieve free, source-linked decision methods and optional decision packs through MCP tools such as catalog, method, sample_pack, and pack_offer. It operates without model calls and disables payments by default.

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.