"A server for planning travel itineraries and trips" matching MCP servers:

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

Lets a coding agent hand a plain-language app idea to a hosted AI software team that shapes, designs, builds, tests and deploys it, returning a one-time link into the full build workspace. Also lists your apps with their stage, pending approvals and live addresses after a standard OAuth sign-in.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Bridges Claude Code CLI with Cursor IDE to use your existing Claude subscription without separate API costs.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Enables AI coding agents to read and write a user's Figma file through the Figma Plugin API, offline and privately, without API tokens or rate limits.

The most advanced MCP server for Godot Editor that lets AI assistants operate directly inside your running Godot project for scene creation, script generation, UI authoring, and more.

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

Enables MCP clients to run Cline CLI prompts and isolated repository-wide analyses, returning terminal text responses with model, duration, and usage/cost.

Provides 23 modular tools for competitive programming, including problem analysis, algorithm planning, code generation, verification, testing, code review, and learning assistance.

A policy layer between ChatGPT and the official Codex MCP server, adding safety controls like read-only sessions, approval policies, and secret scanning for secure daily use.

An MCP server that enhances Claude Code with evidence-first engineering, protocol gates, project-aware verification, auditable traces, and durable autonomous execution.

Enables Claude to hand scoped, implementation-only coding tasks to any model available in a local opencode install — including Kimi, GPT, Gemini and local models — and to run many of them in parallel. Claude stays the orchestrator: it briefs each worker, collects results and needs-flags, then reviews diffs and runs tests, lint and builds itself.

dev-brain-mcp

Enables Codex to start the local DeepSeek Harness Web UI, delegate coding tasks into visible sessions, and review workspace changes.

Provides token-efficient code retrieval for coding agents by indexing repositories and enabling ranked snippet search, symbol outlines, and surgical line reads.

Enables Sublime Text to integrate with Claude Code via the IDE protocol, providing in-editor diff review, selection context sharing, and @-mentions through a local WebSocket/MCP server.

An authenticated OpenAI-compatible inference gateway with an async job broker that bridges a coding agent to a ChatGPT MCP worker, enabling ChatGPT to process inference requests via a tunnel.

This MCP server is designed for planning with Claude Code, Cline, or Cursor and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. It uses the Qwen 3 Coder model for high-quality code generation and can be embedded in IDEs.

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.

Enables a planning agent to delegate research and implementation to lower-cost worker models in isolated git worktrees, monitor job progress, respond to blocked jobs, and land verified changes for review.

MCP server that lets Claude Code ask GPT Codex for adversarial planning, code review, debugging, research, and risk triage without leaving your project workflow.

Enables structured multi-model AI planning sessions across multiple CLI coding tools, orchestrating independent planning, peer review, and final synthesis.

Turns any MCP-over-stdio client into a supervisor for a long-lived DeepSeek Harness coding agent: dispatch a goal, interrupt or steer it mid-flight, and collect results — with conversations that survive process restarts and crashes. Also supports running many conversations at once, approval round-trips, and completion notifications instead of polling.

Enables planning with Claude Code and high-speed code generation using Cerebras, avoiding API limits while producing quality code with visual diffs.

Enables Codex to delegate coding tasks to Grok Build CLI, with Codex handling planning and review while Grok executes.

Autonomous coding pipeline exposed as an MCP server: plan, dispatch, review, and merge software stories through worktree-isolated agents. A frontier model (Claude) handles judgment — planning, review, risk adjudication — while a local model does the implementation, gated by TDD and a merge-time test rerun on the rebased branch.

MCP server for orchestrating coding tasks with frontier-model planning and local execution, enabling manifest validation, plan submission, status checks, cancellation, and report retrieval through Claude Code.

Enables Claude Code and OpenAI Codex CLI to act as bidirectional partners by letting each ask the other for code review, explanations, planning, performance analysis, and implementation help. It also provides background job tools for starting, observing, waiting on, and cancelling delegated tasks.

Streamlines development workflows through AI-assisted codebase analysis, comprehensive planning, task breakdown with dependencies, and automated implementation verification. Enables systematic approach to complex development tasks like framework migrations and feature implementation.