"A tool for converting PDF files to text format" matching MCP servers:

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.

A Model Context Protocol server that enables AI agents to read, search, and extract content from PDF files.

A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.

Enables a persistent local library for mathematical PDFs with cached LaTeX extraction, full-text search in English and Greek, and page/image retrieval over MCP.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

A local RAG MCP server for PDF development experience, enabling document ingestion, semantic search, and Q\&A with source citations using TF-IDF and cosine similarity.

This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.

A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.

Enables LibreChat agents to manage per-user private files, persistent documentation, RAG indexing and semantic search, and publish files via secure opaque public links.

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

A universal MCP tool gateway that manages multiple remote tool processes, enabling LLMs to dynamically invoke web scraping, summarization, and knowledge retrieval through standardized Function Calling.

Enables agents to extract clean structured JSON tables from any PDF with per-cell citations to the source page.

Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.

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.

An MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.

Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

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.