"A service or tool to help organize a trip" matching MCP servers:

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

A unified HTTP/HTTPS service that exposes Model Context Protocol (MCP) functionality over HTTP endpoints, allowing clients to list available tools and call them through a standardized API.

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.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

Gives AI coding agents typed, mode-gated access to a real WordPress site — content, media, users and settings via core REST, plus PHP execution, SQL queries, filesystem operations and Elementor page read/build/patch/snapshot via a companion bridge plugin. Destructive actions and code execution are guarded by feature flags, explicit confirm literals, dry runs, redaction and audit logging.

MCP server that integrates Google Gemini CLI with AI assistants, enabling large file analysis and natural language queries without an API key.

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.

Enables AI assistants to interact with multiple AI models through the OpenCode CLI with a unified interface. Supports plan mode for structured analysis, flexible model selection, and natural language queries with file references.

An MCP server that allows Claude Code to interact with the OpenAI Codex CLI.

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

This is a Model Context Protocol (MCP) server that allows AI assistants to interact with the OpenCode CLI. It enables AI assistants to leverage multiple AI models through a unified interface, with features like plan mode for structured thinking and extensive model selection.

A FastMCP server that dynamically loads tools from Python/YAML file pairs, acting as a stand-in for the Tools Framework in agentic-assembly-framework.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

A 3-tier relay service that serves MCP tools via WebSocket, including calculator, scripture lookup, and location-based recommendations.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Connects ChatGPT to Roblox Studio's built-in MCP, enabling natural-language reading and writing of game data, including executing Luau code and modifying scripts and instances.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

A service that processes natural language requests to perform mathematical calculations by integrating Claude AI with a calculator API through the Model-Context-Protocol architecture.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

A Model Context Protocol implementation that plays sound effects (completion, error, notification) for Cursor AI and other MCP-compatible environments, providing audio feedback for a more interactive coding experience.

An MCP server implementation that helps Claude remember and learn from programming errors by storing, retrieving, and finding similar errors with full context.

Enables a bot to inspect, query, create, edit, and script Roblox Studio instances in token-cheap batches, plus run Luau, playtests, screenshots, and inputs through Roblox's official Studio MCP connection. It runs locally over stdio or an authenticated local HTTP endpoint, returning small structured answers instead of dumping whole games into context.

A Model Context Protocol (MCP) server that converts LaTeX mathematical expressions to MathML format.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.