"Best business class flight prices from DFW airport" matching MCP servers:

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

Enables coding agents to run multiple approaches or tasks in parallel on isolated Daytona sandboxes, returning tested diffs and benchmark rankings so the best outcome can be applied.

Enables AI models to write, format, compile, lint, and structure modern Fortran code using best practices and industry design patterns.

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.

A local MCP server that enables users to evaluate Pharo Smalltalk expressions and retrieve system information via NeoConsole. It provides comprehensive tools for inspecting class definitions, method sources, and system metrics within a Pharo environment.

A best-effort universal logic and numerical solver interface using MCP that implements the 'LLM sandwich' model to process queries, call dedicated solvers (ortools, cvxpy, z3), and verbalize results.

A TypeScript starter template for building MCP servers with example tools (echo, math operations, time, flight status) and resources (server info, greetings). Provides a modular architecture for easily extending with custom tools and resources.

An MCP server that enables AI models like Claude to interact with the Trading Simulator API for checking balances, viewing prices, and executing trades with automatic chain detection.

A CLI tool that runs a Model Context Protocol server over stdio, enabling interaction with specification documents like business requirements, product requirements, and user stories for the Specif-ai platform.

A server implementation of the Model Context Protocol that enables calling iFlytek workflows, supporting complex orchestration modes like sequential, parallel, and looped execution across 14 node types for diverse business scenarios.

Lets agents run a small JavaScript program in a sandboxed worker that composes typed Jev judgments — claim checking, text screening, best-item picking, label classification, passage comparison, and yes/no questions — in a single call, returning only schema-validated results. A companion search tool exposes each judgment's JSON input and output schemas.

A starter template for building remote Model Context Protocol servers using TypeScript, providing modern tooling and best practices while leveraging the MCP TypeScript SDK.

A unified MCP server providing AI agents with 40+ developer APIs including geolocation, crypto prices, DNS lookup, and web scraping. Enables natural language access to various tools through a single gateway.

MCP server for InterSystems IRIS that provides 8 tools including ObjectScript execution, class compilation, and unit testing with a fast DirectTestRunner.

Enables AI coding agents to safely run commands, install dependencies, test, and modify code inside disposable, policy-enforced Docker sandboxes isolated from the host, returning results and diffs.

Enables AI coding agents to safely execute commands, run tests, and modify project files inside disposable, policy-enforced Docker sandboxes that are isolated from the host machine and its credentials.

Run real Playwright E2E tests from your AI coding agent.

Gives AI coding agents typed, bounded tools to boot QNX OS 8.0 QEMU targets, build with the QNX SDP toolchain, deploy and run programs under timeouts, and pull verified source-level backtraces from dumper core files. It also returns process/thread state and slog2 log output as structured JSON instead of free-text shell output.

Control the Unity Editor from any MCP host. One package install, one click, and your AI assistant can see your scene, create objects, and talk to the console. Patina is a Rust MCP server paired with a C# Unity bridge. It connects your favorite AI coding tool directly to the Unity Editor over a local TCP channel, with zero manual config.

Enables AI coding agents to compile Delphi projects programmatically by parsing .dproj files, executing the Delphi compiler, and returning structured error results with multi-language support and automatic configuration generation from IDE build logs.

Backtest PineScript v6 strategies locally from your AI agent: transpiles Pine to C++ and runs it on the deterministic, TradingView-validated PineForge engine via Docker. Tools for single backtests, parameter-grid sweeps, and Binance OHLCV fetch — no API key, data never leaves your machine.

MCP server that exposes a pool of Claude SDK agents as tools, enabling deterministic agent creation, retrieval, and resumption from a capability corpus.

Lets an AI application instantiate, connect to, drive, capture output from, and interrogate a real BBC Micro emulator.

Stdio MCP Server wrapping custom Python runtime (LocalPythonExecutor) from Hugging Faces' smolagents framework. The runtime combines the ease of setup (compared to docker, VM, cloud runtimes) while providing safeguards and limiting operations/imports that are allowed inside the runtime.

Local MCP server that wraps the headless Claude Code CLI as MCP tools, providing stateless access to Claude's coding capabilities through prompt-based interactions. It enables users to execute Claude Code commands with various prompt formats and structured outputs directly from MCP clients.

Remote-control Unity Editor from any AI IDE via file-based IPC — 62 tools, zero dependencies

MCP server that gives AI coding agents full control over Cypress test execution, allowing them to run, debug, and iterate on E2E tests directly from MCP-compatible agents.

An MCP server that provides tools for getting second opinions or detailed code reviews from Gemini 2.5 Pro by embedding file contents in prompts, allowing it to handle large codebases with extensive context.

Provides structured, sandboxed test and lint feedback for coding agents, returning compact typed verdicts with failure fingerprints instead of raw runner output. It enables impact-selected test execution in isolated containers, distinguishing pre-existing failures from regressions.

Enables AI agents to run a project's pytest, unittest, jest, or vitest suites and get back compact structured results with trimmed tracebacks, collection errors kept apart from real failures, and the slowest tests. Agents can then re-run just the failures, expand one failure with local variables, repeatedly execute a single test to judge flakiness, and inspect Python coverage gaps, all under a hard timeout that kills the whole process tree.