"Files" matching MCP servers:

Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

A comprehensive MCP server providing tools for AI agents to interact with code, including reading symbols, importing modules, replacing text, and sending OS notifications.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

Enables Claude.ai to interact with the Cursor editor to read files, write code, get selections, and more.

Enables coding agents to open files and git diffs directly in VS Code, allowing users to review code changes without leaving their conversation.

Enables agent-style remote development over SSH with file operations and command execution, exposing native tools for searching, editing, and transferring files on remote machines.

Enables AI coding agents to conserve tokens by enforcing rules like delegating heavy work to subagents, checkpointing before token limits, and grepping before reading files.

A project intelligence platform for AI coding agents that scans, audits, generates, and monitors context files (CLAUDE.md, AGENTS.md, etc.) with drift detection and repair capabilities.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

A production-ready MCP server that exposes Aider's code editing capabilities via WebSocket, enabling natural language commands to create and edit files with isolated workspaces.

MCP server that parses .sketch design files into CSS-aligned, LLM-friendly JSON, enabling AI coding agents to generate high-fidelity HTML, Vue, or React code.

Enables coding agents to execute Python code, run script files, and install pip packages locally via MCP.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

AI code review against your team's own rules from your repository, before the pull request: two tools, review_changes and get_team_rules. Closed-source binary distributed via GitHub Releases; published in the official MCP Registry as dev.reviewgate/reviewgate.

Long-term memory for Claude Desktop and Claude Code over plain markdown files on your machine. It returns nothing when the corpus has no answer, naming the terms that appear nowhere — no least-bad match dressed as an answer. And it verifies cited commit SHAs against git, so an agent can tell what actually shipped from what was only discussed and abandoned. Local; nothing is transmitted.

* taniwhaai/arai 🦀 🏠 🍎 🪟 🐧 - Policy enforcement for AI coding agents derived from existing instruction files (CLAUDE.md, .cursorrules, .windsurfrules, .github/copilot-instructions.md) — no separate YAML to maintain. Rules with prohibitive predicates (never, forbids, must_not) emit permissionDecision: deny to block tool calls in Claude Code; advisor

Lets Claude Code delegate bounded coding tasks to a GLM model as a sub-agent through a single MCP tool that reads, writes, edits, lists directories, and runs shell commands inside a specified project folder. Each run returns only a short report with changed files and token usage, keeping GLM's intermediate tool traffic out of Claude's context.