"How to work with local files" matching MCP servers:

Enables ChatGPT to work directly on local projects by reading files, editing code, running tests and commands, inspecting Git state, and managing durable workflows, with optional delegation to local Codex agents for complex tasks.

Enables AI assistants to access and inspect local files within a specified sandbox root.

Enables uploading local files to Slack with a message, supporting channels, DMs, threads, and file titles.

Lets MCP clients delegate private or bulk text tasks to a local OpenAI-compatible LLM while keeping data on the machine.

Enables Claude to fetch web pages from the user's own local network connection instead of AI datacenter IPs, including optional authenticated sessions, with verdicts for bot challenges, login walls, and other failures.

Twelve tools combine per-function complexity and branch coverage to rank work and check edited functions against their ceiling. Packets include source, uncovered lines, change-coupled files and near-duplicates. MCP calls can write local caches and metadata; they do not run test suites, edit source or claim work.

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.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Enables local conversion of PDFs to Markdown, with automatic OCR fallback for scanned pages, image extraction, and page-by-page structure preservation.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

Provides AI agents with accurate current time and date, including the Jalali (Persian) calendar, timezone-aware with automatic DST handling, via an MCP server.

Lets AI assistants work on sensitive files by reasoning over schemas and masked output while deterministic local code handles raw data, never exposing real values to the model.

Enables AI agents to search, read, annotate, and manage a local Zotero library via the Model Context Protocol, with full local storage and no cloud dependency.

Provides a stateful local Spark session for running PySpark and SQL cells, enabling local data exploration before deploying to Microsoft Fabric.

A local MCP server that saves work-in-progress checkpoints to SQLite, enabling AI agents to resume interrupted sessions without redoing work.

A read-only MCP server that lets AI search and read both sides of a user's own LINE Desktop chat history on macOS using local database snapshots, without needing a LINE Official Account or altering unread state.

Enables local document search and reading across formats like docx, pdf, md, and more, providing tools for listing, searching, and reading documents.

Enables cloud-based MCP clients to delegate privacy-sensitive or large-output tasks to a local LLM, returning sanitized placeholders or digested summaries so private data never leaves the user's network.

A read-only MCP server that lets AI search a user's own LINE Desktop chat history on macOS, providing tools to list chats, retrieve messages, and search conversations directly from the local encrypted database.

A thin MCP server that delegates lightweight tasks from Claude Code or any MCP-compatible client to local or cloud LLMs via LiteLLM, supporting models like Ollama and cloud APIs as subagents.

Enables natural-language semantic search over your own local files through an MCP server, fully offline without API keys or a server daemon, with optional LLM-grounded answers and exact file:line sources.

Read selected local folders from ChatGPT on macOS, with per-folder read-only or read-write access and hash-checked, recoverable edits. Requires local installation and a private ChatGPT app. The Glama container is a synthetic evaluation demo; it does not connect to files on your Mac.

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.

Delegates mechanical text-to-text tasks to a local LLM to save Claude subscription quota, reading files server-side so large content never enters Claude's context.

Enables offline local LLM analysis of text data via MCP, taking raw text and an instruction to return analysis results without cloud calls.

A simple MCP server that reads local images and returns them as ImageContent for LLM vision analysis.

MCP server that gives MCP clients access to a personal Telegram account. It can read chats, search messages, download media, and send messages or files while keeping credentials local.

Connects an MCP client to an existing classic Outlook session on Windows so agents can browse stores and folders, list recent messages, run literal text and metadata searches, and read plain-text bodies with attachment metadata. All access is strictly read-only, with no sending, deleting, moving, or network connections of its own.

Enables Claude Cowork on Windows to read, write, edit, list, find, and regex-search files inside a WSL distribution over stdio, accepting Windows and Linux path spellings. Access is confined to server-fixed roots, with secrets files refused, key-shaped strings redacted, and no delete or move operations.

Local Dev Doctor turns an unfamiliar development checkout into an evidence-backed diagnosis. It discovers how the project is meant to run, checks safe prerequisites, and executes only the exact probe that a user approves.