"file system" matching MCP servers:

Automates file system operations through natural language commands using a combination of LLM (Ollama), MCP tools, and a Python bridge agent.

Provides file system access for resume matching through JSON-RPC, enabling semantic search and workflow orchestration with LangGraph.

Enables natural-language search over locally indexed files such as markdown, text, images, videos, and PDFs, and retrieves indexed text or media metadata by path. It lets Cursor query a local embedding index built with Gemini and SQLite.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

An automated system that processes raw text through a three-stage pipeline to assess data quality, enhance information, and generate structured knowledge graphs. It provides tools for triple extraction with confidence scoring and creates interactive HTML visualizations of the resulting graph.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

Exposes Dropbox Dash search and file metadata via STDIO, enabling users to search across company content and fetch detailed file metadata and content.

Enables interaction with the Morphik multi-modal database system for document ingestion, retrieval, querying, and management. Supports text and file ingestion, semantic search with LLM-powered completions, and file system navigation with security controls.

MCP server that allows AI agents to read, search, summarize, and answer questions about local PDF, DOCX, XLSX, and TXT files through five tools.

Provides AI clients with a persistent, readable and writable long-term memory layer over MCP, enabling creation, retrieval, tagging, updating, and deletion of memories across sessions.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

A Model Context Protocol (MCP) server that converts natural language queries into SQL statements, allowing users to query MySQL databases using conversational language instead of writing SQL code.

Enables natural language queries over financial reports, with multimodal retrieval across text, tables, and charts via an agentic RAG system.

A local RAG-powered documentation search system that uses vector embeddings and Qdrant to enable semantic search across markdown, HTML, and other file formats. It provides an MCP interface for AI tools like Cursor to intelligently query and retrieve information from local knowledge bases.

A memory system for agent

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.