"XML" matching MCP servers:

A Model Context Protocol server that provides tools for code modification and generation via Large Language Models, allowing users to create, modify, rewrite, and delete files using structured XML instructions.

An MCP server that gives AI assistants direct access to .NET test-coverage tooling, enabling them to run tests, parse Cobertura XML, identify uncovered branches, diff coverage between runs, and append test code.

Enables AI agents to write and debug World of Warcraft: Infinite addons end-to-end: statically checking Lua, .toc and XML against the client's own API manual, hot-loading changes into the running game, then executing Lua or slash commands and reading back errors with stacks, print output, events, frame info and screenshots. Also records events, inspects frames and keeps versioned history with one-click rollback.

A tool that generates various UML diagrams through natural language descriptions or PlantUML code, functioning as an MCP server that can integrate with clients like Claude.

Enables LLMs to manage and run machine learning training jobs on a remote server, including syncing code, submitting experiments, monitoring progress, reading TensorBoard metrics, and receiving completion notifications.

An implementation of the Model Context Protocol for the Opik platform that provides a unified interface for accessing Opik's capabilities, supporting multiple transport mechanisms for flexible integration with IDEs and other environments.

A server that integrates trained Random Forest models with Bee Framework, enabling ReAct interactivity for AI tools and agents.

Gitbub mcp

Execute Python code with persistent state across Claude conversations using a background Jupyter kernel. Supports creating notebooks, managing virtual environments, and maintaining variables/imports between code executions.

Enables routing of ML tasks like chat, sentiment analysis, recommendations, and summarization to appropriate models through a dynamic YAML-based registry. Provides async FastAPI endpoints with streaming support, retry logic, and pluggable model architecture for scalable ML inference.