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

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.

A local, read-only stdio MCP server that lets a Grok Build CLI agent check how much of its weekly SuperGrok / Grok Build usage pool is left, when the period resets, and its Extra Usage cap via tools like get_budget. It reads the existing grok login session and returns per-product figures as JSON, mirroring the TUI /usage screen, so agents can pause before expensive work when quota runs low.

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.

Drives the browser you are already signed into, so work behind a login, SSO or MFA needs no scripted auth. A local stdio MCP server talking to the free T3rnel Browser extension over a native-messaging relay. 14 tools standalone; 101 with the extension — CSS-as-component export, full-page capture, React inspector, network and console capture, form filling, record and replay. Nothing hosted.

Evaluates task suitability for local models before cloud API calls, routing to Ollama or similar to reduce costs.

Enables an AI agent to delegate self-contained tasks to other agent CLIs already installed and signed in on the same machine — Claude, Codex, Gemini, or a local Ollama model — using their existing subscriptions with no API keys. Supports synchronous single-call delegation plus background tasks that can be started and polled for longer-running work.

Connects any MCP host to the Haven Agent Gateway, enabling session creation, agent discovery, collaboration, handoff, and work operations through MCP tools over stdio or Streamable HTTP.

Enables Codex to delegate long-running work to Claude Code via blocking MCP calls, with tools for model discovery, run, and resume without polling.

An MCP server that lets AI coding assistants delegate work to Capy's cloud agents and wait for results, all within the editor.

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.

A local MCP server that lets Claude Code orchestrate Codex CLI and OpenCode CLI as sub-agents with strict security, concurrency, and output limits.

MCP bulletin board server for coordinating file ownership across multiple AI coding agents (Claude Code, Codex, OpenCode, Trae). It enables agents to claim files, check conflicts, and post decisions before starting work to prevent collisions.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

An MCP server that splits coding work between ChatGPT web (planning, review) and Codex CLI (implementation), enabling agentic workflows over a local repo with risk-gated execution and safety rails.

Read-only MCP server that lets an AI agent search a catalogue of MCP servers, agent skills and open-source AI projects, with provenance: how many sources carry an entry and when it was first and last seen. Zero dependencies, single file. Remote endpoint https://anclum.com/mcp

Local-first MCP proxy with BM25 tool discovery, quarantine security, Docker isolation, OAuth support, activity logging, and web UI. Routes multiple upstream MCP servers through a single endpoint.

Enables coding agents to run persistent, budgeted work sessions with dependency graphs, human decision queues, required validation, and clean handoffs for autonomous or human-in-the-loop execution.

Enables a main agent to delegate tasks to reusable Pi agents with persistent sessions, question handoffs, bounded waiting, and in-run correction, while continuing other work. It supports named agents, context retention across MCP restarts, and result retrieval through MCP without repeated polling.

A local MCP bridge that lets a compatible host start, monitor, steer, interrupt, resume, and approve Codex app-server work while enforcing repository-root security.

A small local MCP adapter that lets Claude Code delegate bounded research work to Codex plan credits without giving the worker ownership of the final report.

A lightweight, zero-dependency Python MCP server that exposes a single do_everything tool. Any AI agent that supports MCP can use it to offload all language-model work to a local OpenAI-compatible endpoint, reducing cost on the agent's primary LLM.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Last-mile human work for AI agents. Phone calls, physical-world tasks, captcha on an already-authorized account, subjective QA, and long manual work, done by real virtual assistants.

MCP server that delegates Claude Code subagents to alternative backends such as local models, DeepSeek, or AWS Bedrock, while keeping your Claude Code orchestrator session intact.

AutoVault is an MCP server that provides a local-first vault for AI agent skills, enabling validation, signing, and serving of SKILL.md files to agents through stdio or HTTP.

Enables AI agent hosts to exchange durable, local sender/receiver messages through a SQLite mailbox exposed as MCP tools (connect, send, receive, reply, ack, status, disconnect), so a coordinator can dispatch commands and an executor can return results without model polling while idle. A configured host wake adapter is still required to turn an incoming event into an Agent turn.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

Enables agents to monitor token/usage limits and persist work checkpoints, allowing them to pause, resume, and track progress across sessions.

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.