"Tools for Local File Read and Write in Minecraft Coder Pack (MCP)" matching MCP servers:

Provides tools for searching and reading local files, supporting metadata filters, PDF full-text keyword search, and sandboxed file access.

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.

Provides MCP tools for workspace initialization, health checks, context recall, and durable memory capture, enabling project-specific memory and shared company knowledge with citations.

A Model Context Protocol server that provides safe, intelligent read/write access to NetBox instances, designed with safety-first principles for Large Language Model automation.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

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

Enables read-only semantic search over a local document corpus with on-device embeddings and a local Chroma store, featuring symlink-hardened file access and structured error handling.

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.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

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.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables developers to index and search their own local PDF, DOCX, Markdown, and text documents from an MCP client or terminal, combining semantic similarity with keyword matching for exact technical terms. Everything runs on the user's machine with no API key, Docker, or external database, so private documents stay private and work offline after the initial model download.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

A collection of tools that enhance MCP-based workflows with caching, retry logic, batch operations, and rate limiting capabilities.

A local MCP server that indexes codebases using Tree-sitter AST parsing and LanceDB, enabling AI models to search across repositories with sub-second latency.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.

Turns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.