"Using Jira MCP to comment on subtasks, read descriptions, and create a test plan" matching MCP servers:

A dynamic service that creates and manages Model Context Protocol (MCP) servers, allowing users to spawn, customize, and control multiple MCP servers as child processes.

Enables running a stateless Model Context Protocol server over Streamable HTTP on Cloudflare Workers or any fetch-capable runtime, with signed sessions, origin checks, and JSON-RPC request handling. It lets developers define tools with JSON Schema validation and per-request context.

Enables Claude Code, Cursor, and Codex users to orchestrate plan/build/review workflows across host or CLI models with a spec gate, tool vetting, hard per-run and per-day cost/token limits, safe defaults, and clear logs.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents and MCP clients to fetch and crawl webpages, strip noise, and convert them into token-efficient Markdown while preserving links and supporting caching, robots.txt, concurrency, and depth.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Fetches webpages and crawls sites locally, stripping noise and converting content into clean, link-preserving Markdown with a minimal token footprint. Supports configurable crawl depth, concurrency, robots.txt compliance, and caching for efficient AI agent and LLM pipelines.

Enables AI agents and MCP clients to fetch webpages, strip noisy content, and convert them into clean Markdown with link preservation and optional multi-page crawling, using caching and polite rate limiting for efficient scraping.

A simple MCP server combining FastMCP and LangGraph that enables AI agent interactions through tools for asking questions to an OpenAI-powered agent and receiving personalized greetings.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

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.

Enables MCP clients to interact with A2A agents through four ordinary tools, translating agent discovery, messaging, task reading, and cancellation between the A2A and MCP protocols, with support for the tasks extension.

An MCP server that turns a one-sentence idea into working, tested code using a pipeline of six AI agents, with optional deployment.

Enables MCP clients such as Claude Desktop to hand tasks to a running DeepSeek Harness window, where each task becomes a real session visible in the sidebar with a reusable session id. If the in-process plugin is unavailable, it falls back to running the task headless so the tool still returns a result.

Turns any OpenAPI/Swagger specification into a functional MCP server, dynamically exposing API endpoints as LLM tools or generating standalone Python code.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Converts any REST API into MCP-compatible tools instantly by providing an OpenAPI/Swagger spec, enabling seamless integration with AI agents.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables LLMs to discover and invoke generated API actions as MCP tools and read context through MCP resources for list and get-by-id operations. It provides HTTP-based MCP access for clients such as Cursor and Claude without extra glue code.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Acts as a bridge between standard REST/OpenAPI web APIs and the Model Context Protocol, dynamically converting REST endpoints into MCP tools for LLM clients like Cursor, Claude Desktop, and n8n.

This MCP server enables AI agents to interact with recruitment platforms like BOSS直聘, 猎聘, and Moka through a Chrome extension via WebSocket. It provides tools to detect platforms, fetch candidate lists, and test extension connectivity.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Enables an IDE agent to receive only the Product Owner-approved decisions, technical brief, plan and test cases from a local multi-role planning chat, then work through stories with MCP tools such as get_next_story, update_story and ask_po. Blocking questions can be raised back to the chat so the agent waits for a human answer, and story completion is refused until test cases are reported.

Enables agents to dynamically allocate test-time compute, including entropy-driven thinking token budgets, temperature annealing, and reflection passes, while verifying reasoning via MCTS search with process reward modeling, counterfactual backtracking, formal proof checking, and majority-vote consensus. Runs as a zero-dependency Python MCP server over STDIO JSON-RPC for integration with Claude Desktop, Cursor, or custom agent runtimes.

Lets AI agents register, post job specs to matching specialist bots, authorize PayPal escrow funds, and capture payment only for units that pass acceptance checks. Covers the full job lifecycle — bidding, hashed contracts, funding, delivery verification, payouts, dispute mediation and long-polled events.

Enables any MCP client to operate DeepSeek Harness sessions, execute tasks, and manage workspaces through an MCP server running inside the dsh process.

Enables deployment of a remote Model Context Protocol server on Cloudflare Workers without authentication. Allows custom tool definitions and connection from MCP clients like Claude Desktop or Cloudflare AI Playground.