"Movies available on Netflix" matching MCP servers:

From ticket to reviewed pull request, on your own machine

A tiny Hermes-friendly bridge that exposes oh-my-pi's CLI as MCP tools (prompt and doctor) for code generation and environment verification.

Enables an external AI agent to delegate tasks to a local Claude Code installation and receive structured reports, with code modifications governed by a plan-approval-execution workflow and direct file and git operations available outside that loop.

Enables multiple AI coding agents to collaborate on a project by coordinating tasks, file leases, and messages through a shared hub, preventing conflicts and enabling parallel development.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Exposes models available on the gm inference gateway as callable tools, enabling coding agents to query models and list available ones via natural language.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

An MCP server that lets an AI coding agent delegate implementation work to the OpenCode CLI, choosing an explicit provider + model on every call.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Visual feedback for AI coding agents on Astro sites, providing live annotations with source file:line, CSS selector, Tailwind classes, and computed styles.

Persistent memory and pattern learning for AI coding assistants via MCP.

Two MCP endpoints for building cognitive AI agents. /blueprint/mcp serves a 9-chapter architecture blueprint (4 tools). /devops/mcp serves a 7-chapter coding agent playbook (3 tools). Each product has its own Gumroad license key. Free chapters available without auth.

MCP server that lets multiple coding-agent sessions on the same machine discover each other and collaborate through a shared SQLite database.

An MCP server for AI-to-AI collaboration via the Gemini CLI. Available on npm: ask-gemini-mcp. Works with Claude Code, Claude Desktop, Cursor, Warp, Copilot, and 40+ other MCP clients. Leverage Gemini's massive 1M+ token context window for large file and codebase analysis while your primary AI handles interaction and code editing.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

A tiny always-on server that coordinates multiple coding-agent sessions on one project (a 'show'), using a worker/director pattern with polling, leases, and SQLite state.

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.

Enables AI coding agents to retrieve relevant execution context before a task, compare candidate paths, log outcomes, and search past experiences stored locally in SQLite. It lets agents reuse what worked on similar tasks and get live alerts when a run stagnates or repeats failures.

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.