"How to read document contents" matching MCP servers:

Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.

Enables NPC agents to read, write, search, and manage a shared corporate document store, and to turn Mission Scripts into structured report outlines and scaffolds aligned to strategic intent.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Search reusable technical experience from AI agents, inspect evidence and limitations, and discover public agent Trust Passports through seven anonymous read-only MCP tools. Apache-2.0 integrations, examples and documentation connect to the separately governed hosted Remnant service. Agent OAuth and Candy are separate surfaces.

Verifiable document intelligence for AI agents. Extract text, tables, and structured data from PDFs and URLs. Summarize, answer questions, check claims, and translate — all with cited evidence. Store tamper-evident evidence bundles with cryptographic signatures and on-chain attestation via Base L2. Cross-document semantic search and Q&A across named collections. Pay per call with USDC

Self-hosted AI companion whose MCP server lets Claude, Cursor and other clients search her long-term memory, document library and notes, read today's plan and to-dos, and ask her questions. Read-only by default; write tools follow her approval settings.

Enables a language model to query a document index through callable tools: ranked passage search, reading a chunk with its neighbours or a full document by id, index statistics, and sourced generated answers. The tools chain together, letting a model search, then read around the best chunk ids it received.

Provides read-only MCP tools for searching and asking over private documents via a local RAG service (reed), returning ranked passages with citations while keeping data on the machine.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Local-first, read-only MCP server for retrieving and searching authorised CCSDS and ECSS standards, enabling citation-rich passage-level search and document access.

An MCP server that allows users to efficiently search and reference user-configured documents through document listing, grep searching, semantic searching with OpenAI Embeddings, and full document retrieval.

Enables AI agents to connect local knowledge bases, preview and index selected directories, search for traceable snippets with source metadata, and read indexed document versions through MCP tools.

A read-only MCP server for your paperless-ngx document archive that lets an AI assistant search documents by full text, tags, correspondents, and dates, and read the text of any document.

Enables AI agents to access a markdown wiki or knowledge base through per-topic tools that expose available domains and retrieve tables of contents, matching lines, or full pages on demand.

Local RAG knowledge base for Qwen Code, enabling document indexing and semantic search via MCP tools. Supports metadata filtering and document retrieval without external dependencies.

Provides tools for document conversion, processing, and generation, enabling PDF to structured JSON conversion, document creation, and caching for improved performance.

Enables read-only document retrieval and SQLite exploration through tools over stdio, enforcing a safety gate before SQL execution.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

A local document evidence layer for MCP clients that ingests documents (PDF, Office, images) with optional OCR, indexes them in SQLite FTS, and provides retrieval tools with source coordinates.

MCP server that indexes uploaded PDF, DOCX, and TXT documents into an isolated per-session in-memory vector index and retrieves the exact matching passages behind each answer. It exposes document indexing and search tools to a Q&A backend so every response is grounded in cited, retrieved evidence.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables local semantic search over PDF, DOCX, PPTX, and EPUB documents by converting them to Markdown, indexing them in ChromaDB, and retrieving complete structure-aware sections with tables and equations.

Serves a domain's knowledge as a routing table: the agent reads one line per area saying when it is relevant, picks, and only then opens documents. Tools walk the table, read a document, and read another backbone over a temporary link.

Local-first, read-only MCP server for searching and retrieving cited evidence from archived Telegram chats, including transcripts and media metadata.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

A server that enables LLMs like Claude to query AWS DynamoDB databases through natural language requests, supporting table management, data querying, and schema analysis.