"filesystem" matching MCP servers:

A Model Context Protocol server that provides AI agents with secure access to local filesystem operations, enabling reading, writing, and managing files through a standardized interface.

Enables local, event-driven messaging between multiple AI coding-agent sessions on the same machine using only Node.js and the filesystem, allowing them to hand off tasks, exchange replies, and broadcast announcements without external infrastructure.

Local inter-agent messaging for AI coding agents via filesystem relay.

Enables AI agents to interact with Kubernetes clusters through progressive disclosure, where agents discover TypeScript modules via filesystem, write and execute code, and receive summarized console output for cluster management tasks.

Lets a frontier coding agent delegate research, cataloguing, and long-running computation to a local LLM with guarded filesystem, web, and Python execution tools, preserving the agent's context and tokens.

Bridges MCP (Claude Code) to ACP (Devin), enabling Claude Code to delegate autonomous tasks to Devin with permission and filesystem sandbox controls.

A unified gateway and dashboard that aggregates multiple MCP servers into a single endpoint for streamlined management by AI clients. It features a centralized YAML configuration, a web-based monitoring dashboard, and hot-reload support for managing filesystem, GitHub, and database tools.

An orchestration system that coordinates multiple MCP servers to eliminate AI session startup overhead through intelligent project caching. It enables instant context loading and conversation continuity by synchronizing data across Memory, Filesystem, Git, and Database MCPs.

Enables structured multi-agent workflows by managing task claims, action logs, handoffs, and health-gated completion while keeping specifications as filesystem Markdown artifacts.

Enables durable, crash-safe control of long-running AI-agent workflows with idempotent operations and scope-aware reconciliation over MCP filesystem and execution tools.

Exposes tools for listing files and reading their contents over stdio, allowing MCP clients to inspect local filesystem paths during agent runs.

Airlock provides named resource locks, atomic shared state, presence, events, and a task queue for AI agents to coordinate on a shared filesystem, with blocking waits and SQLite-backed persistence.

Provides a provider-agnostic MCP server that runs structured multi-agent workflows by owning task claims, action logs, handoffs, and server-side health gates over a shared filesystem and database. It enables coordinated Explorer, Consultant, Builder, and Reviewer roles with tracked specifications and audited completion evidence.

Aggregates multiple downstream stdio MCP servers behind a single endpoint, namespacing their tools so AI clients can discover and call filesystem, GitHub, SQLite, and other tools through one connection. Provides crash detection, auto-restart, configurable timeouts, and structured logging for resilient operation.

Enables AI coding providers to coordinate structured multi-agent workflows with documents, tracked tasks, handoffs, and health evidence. It owns task claims, action logs, and final health results while leaving specification files on the filesystem.

Exposes DeepSeek Harness agent capabilities over MCP so any MCP client can delegate real coding work to a running agent with its full toolset (bash, filesystem, todo, web). Turns are dispatched asynchronously into sessions and then tracked through status, tail, wait, cancel, compact, and rename tools, with the session log as the source of truth so results survive restarts.

Enables a remote MCP client to access narrowly scoped local filesystem resources and process capabilities through a loopback-only bridge. Supports guarded file reads, searches, and mutations, authenticated transport, and private reachability via OpenAI Secure MCP Tunnel.

MCP server that turns your browser into a filesystem. 38 tools let AI agents ls, cd, grep, click, and type through Chrome via the DOMShell extension.

Enables AI coding providers to run structured multi-agent workflows through MCP with tracked tasks, action logs, handoffs, health gates, and filesystem-backed specification documents.

Enables coding agents to reach every configured MCP server through just three tools (search, describe, execute) and a sandboxed JavaScript runtime, so a model writes one short program instead of making dozens of tool calls. Runs without network, filesystem, or credential access, returning only the result.

MCP server that lets an AI agent delegate tasks to Claude Code on a remote Mac via SSH over Tailscale. Persistent sessions, 200k context, full Mac filesystem access.

Enables LLMs to connect to n8n workflows, Blender 3D, Notion, and filesystem tools through a single MCP hub with hot-pluggable bridges. Features a pedagogical transparency layer for educational purposes and supports multiple LLM providers including Claude, OpenAI, Gemini, and Ollama.

Exposes an MCP workflow that lets AI coding providers claim and track tasks, log actions, coordinate lead/explorer/consultant/builder/reviewer handoffs, and manage specification documents on the filesystem. Completion is gated on server-owned health checks, so finished work requires passing verification evidence rather than a provider's own claim.

Enables centralized management and unified interface for multiple child MCP servers (filesystem, sqlite, etc.), allowing users to discover, launch, and execute tools across different MCP servers through a single gateway.

Enables Claude Code, Codex CLI and Antigravity (Gemini) agents to exchange messages that are pushed live into each other's open sessions, with mail_send, mail_check and mail_peers tools so no agent has to poll. Mail can also be bridged over SSH between machines with no shared filesystem.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Enables MCP-compatible developer environments to shrink verbose tool outputs—test logs, git diffs, directory listings, and file dumps—before they reach the context window, preserving compiler errors, failure signatures, and decisive anchors. It runs either as direct callable tools or as a transparent proxy wrapping other MCP servers such as filesystem, git, or terminal providers.

A task-aware transparent proxy that wraps stdio MCP servers (such as git, filesystem, and terminal) and compresses large tool outputs field-by-field using declarative YAML profiles, passing narrow intent queries through untouched. Discarded data is stored in SQLite and can be losslessly retrieved via an injected tool, either in full, by line range, or by grep, while errors are always preserved and failures fall back to the raw result.

A local hub that lets you chat with Claude, Gemini, OpenAI or Ollama while exposing Python-written connector tools (filesystem, web, system, sqlite, plugins) to the dashboard, Claude Desktop and any MCP client over HTTP or stdio. Every outbound webhook, Discord, Slack, email or push action waits for Approve/Deny, prompt templates are published as MCP prompts, and all JSON-RPC traffic is visible in a live inspector.

A local stdio MCP server that lets Claude Desktop hand tasks to a Hermes Agent on the same machine and read the raw output and files back, job by job. Long work runs as background jobs that are polled by id, so Claude can plan, verify against the filesystem, and resume earlier sessions without blocking on tool timeouts.

Gives AI coding providers a shared, provider-agnostic workflow for authoring use cases and specs, claiming and tracking tasks across lead, explorer, consultant, builder, and reviewer roles, recording handoffs, and gating completion on native health scripts. It owns task claims, action logs, handoffs, and final health results over MCP, so teams can move from a request to verified work while specifications stay as Markdown files on the filesystem.

Provides a stdio MCP server that drives structured multi-agent development workflows: claiming and updating tracked tasks, logging actions and handoffs, gating work on server-owned health checks, and managing use-case, functional, and technical specification documents on the filesystem. It also exposes read-only skills for triage, consultation, review, and product guidance while enforcing approval and repair authorizations.

Gives AI coding providers a shared, provider-agnostic workflow server for drafting and approving Markdown specifications, claiming tracked tasks, logging actions, routing role handoffs, and gating completion on verifiable health checks. It works across Claude Code, Codex CLI, Cursor, Grok Build, and OpenCode, keeping documents on the filesystem while task, evidence, and health data live in a configured database.