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

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.

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

Enables document Q&A with grounded citations using the official Gemini File Search API. Upload documents, create stores, and ask questions that return exact passage citations.

Enables face detection and recognition in Agentic AI workflows using Azure Face API, supporting attribute detection, image comparison, and face recognition.

Turn any API into an MCP server. Query in English. Get results—even when the API can't.

Enables image and video generation using Luma's API, supporting multiple models, aspect ratios, and advanced features like keyframes and style references.

Enables Claude Code, Cursor, Codex and other MCP clients to generate and edit images and videos directly in a project using AI subscriptions the user already pays for, without API keys or extra billing. Also provides local media tools such as background removal, video editing for social formats, and automatic resizing and optimization of results.

A Python MCP server that exposes local Ollama models as tools for AI assistants, enabling chat, generation, embeddings, and model management without cloud APIs.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

Enables AI coding clients to generate and save project-aware images and videos using existing paid AI subscriptions, without API keys or passwords, and to optimize media for the web.

Enables natural language queries for device support, including resolving device references, retrieving device details, historical metrics, and checking provisioning status. Maintains conversation context for multi-turn interactions.

Enables AI cost calculation, comparison, and optimization across major providers like Anthropic, OpenAI, Google, Meta, and Mistral. Supports cost estimation, budget-aware model finding, and token estimation through a simple API and MCP integration.

Dynamic multi-agent debate MCP server for structured argumentation and reasoning among AI agents.

An MCP server that enables any AI agent to call Claude using your existing Max/Pro subscription via OAuth, avoiding additional API billing.

OrcaRouter MCP — browse 160+ LLM models with live pricing (no API key needed for catalog) and route chat completions through the OrcaRouter gateway with automatic fallback.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.

Enables AI agents to make pay-as-you-go AI inference requests through x402 with automatic Solana USDC payments, no API keys or registration, while enforcing configurable spending limits.

An MCP server that gives text-only models image generation and understanding capabilities, with tools like generate_image, describe_image, and set_api_key. It enables any MCP client to create and analyze images using a single API key.

Enables MCP clients like Claude to analyze public YouTube videos by asking questions grounded in both audio and visuals through the Google Gemini API.