"Using Jira MCP API in Python" matching MCP servers:

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Enables coding agents to execute Python code, run script files, and install pip packages locally via MCP.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.

Simple sequential thinking MCP in python

Two AIs. One answer. ChatGPT and Claude discuss with each other until they agree, through your own signed-in CLIs: no API keys. Both read your project (never change it) and search the web. Plugin for Claude Code, Codex, Claude Desktop and the ChatGPT app.

Bridges Warp's Oz coding agent to any IDE or MCP client (Claude Code, Cursor, Codex), plus native @oz in VS Code Copilot Chat. Cross-platform (macOS/Linux/Windows), independent project using only Warp's documented public oz CLI + MCP interfaces.

Production-ready MCP server starter templates in TypeScript and Python. Includes tool, resource, and prompt patterns with Claude Desktop integration configs.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Universal Python code execution MCP server that lets LLMs write and run Python for any task, with auto-install packages, streaming output, and automatic file display.

Enables MCP clients to delegate routine coding tasks to a local model in isolated git worktrees, using test results as a gate and the client as the reviewer before changes are merged.

Enables any LLM to drive your local Codex CLI in a sandboxed workspace, running coding tasks and generating images without needing an API key.

Enables delegating coding tasks to a pi agent as a steerable background worker, allowing mid-run redirection, follow-ups, and keeping the delegate's context isolated from your main conversation.

Enables primary AI orchestrators to delegate token-heavy coding tasks to economical models via OpenRouter, running sandboxed autonomous ReAct loops with filesystem operations and git audit tools while reducing token costs.

Exposes pi as a sub-agent for MCP-capable coding assistants, enabling task dispatch, messaging, and interactive dialog handling via tools like pi_dispatch, pi_send, and pi_respond.

Enables AI agents to execute Python, TypeScript, and JavaScript code in persistent Jupyter kernels with stateful variables and imports across interactions.

Enables AI agents to delegate complex coding tasks, refactorings, and automated code reviews to OpenAI Codex and Google Antigravity CLIs, running them autonomously in YOLO mode with process supervision.

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

Enables AI tools to interact with browsers for enhanced frontend development, providing context to LLMs through tools like API call analysis, screenshots, element selection, and documentation ingestion.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Enables a supervisor agent to create, coordinate, steer, inspect, and interrupt multiple Codex agents as a subagent pool within an MCP host, running them alongside the host's own subagents.