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

A Python-based server allowing seamless integration with JIRA for managing and interacting with projects through custom APIs.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Enables AI assistants to manage Jira issues with full CRUD, transitions, linking, commenting, and file attachments via the Jira REST API v3.

An MCP server that lets AI assistants create, search, and manage Jira issues using Chrome browser session cookies for authentication, eliminating the need for API tokens.

An MCP server that reads a file's embedded C2PA Content Credential to report whether it declares AI generation or a real-world capture, and checks whether that declaration's signature still verifies against the file's current bytes.

A Python bridge for interacting with the macOS Messages app using MCP (Multiple Context Protocol). Simple install using uvx mac-messages-mcp

Enables Claude to create local, persistent mock REST APIs on demand from plain-language descriptions, with data written to disk so it survives restarts.

MyChart MCP Server

Serves the Pancake API documentation to AI assistants, enabling them to search endpoints, retrieve full API contracts, webhook payloads, and guides without live API access.

Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.

An async MCP server for JIRA integration, enabling AI assistants to search, create, and manage JIRA issues via JQL and other operations.

Stdio MCP server (Python, standard library only, server.py in this repo) with one tool, analyze_toxicity: scores English text for toxicity, insults, profanity, threats and identity attacks (0 to 1). Perspective API compatible. No LLM.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

MCP server for API discovery from .mind spec files, enabling AI assistants to list, inspect, and call APIs defined in OpenAPI specs.

Enables analysis of bulk RNA-seq data using natural language queries, executing R and Python in a Docker container with automatic sample anonymization and privacy controls.

An MCP server that translates natural-language queries into structured catalog searches using Amazon Bedrock, enabling MCP clients to query existing in-house APIs.

Exposes Jira task management (list, create with two-step confirmation, start) to AI agents while keeping credentials hidden, and deliberately does not handle git operations.

Enables AI coding agents to diagnose and repair Python Selenium UI automation using real evidence from live DOM and test runs, with enforced safety boundaries and verifiable fixes.

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.

Enables coding agents to query AI model API prices, including historical point-in-time lookups with cited sources, using a bundled dated dataset and requiring no API keys.