"Wikimedia Foundation" matching MCP servers:

Atom-first long-term memory server for MCP clients with durable knowledge storage, hybrid search, typed relations, and context packing for AI prompts.

Provides AI coding tools with queryable documentation for the AK (Active Kernel) event-driven MCU framework, including API signatures, guides, and guardrails.

Provides a TypeScript foundation for building MCP servers with stdio and Streamable HTTP transports, including tools, resources, prompts, health checks, and Docker support.

A production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.

Integrates Jina AI Search Foundation APIs to provide web page content extraction and search capabilities. It supports automatic pagination, result caching, and both standard and VIP search endpoints for enhanced information retrieval.

An MCP server for accessing and analyzing Apache Software Foundation Incubator podling data from podlings.xml. It provides tools to query podling metadata, generate statistics, and analyze incubation trends over time.

A simple MCP server implementation that provides AI models with basic Solana RPC methods and helpful development prompts.

Enables AI agents to access and search up-to-date Solana documentation, get canonical spec references, and fix Anchor/Pinocchio Solana programs via MCP tools.

An MCP server that enables AI assistants to generate, score, and analyze DNA sequences using the evo2 genomic foundation model. It supports multiple execution modes including local GPU, SLURM clusters, and the Nvidia NIM cloud API for tasks like variant effect prediction and sequence embedding.

Provides a configurable web server foundation for ioBroker home automation systems to implement Model Context Protocol functionality. Features HTTP/HTTPS support, authentication, and basic REST API endpoints for adapter integration.

A FastMCP server template with full MCP authentication via Descope, designed for easy deployment on Render. It provides a foundation for building secure, authenticated MCP servers with scope-based access control.

An LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.

A production-ready foundation for developing custom Model Context Protocol (MCP) servers in TypeScript with a complete layered architecture pattern. Includes a working IP geolocation example and comprehensive developer infrastructure to connect AI assistants with external APIs and data sources.

A boilerplate for building and deploying remote MCP servers on Google Cloud with authentication, providing a foundation to experiment with MCP tools and capabilities.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

A production-ready, transport-independent foundation for building enterprise MCP servers in Python. Provides infrastructure, configuration, DI, logging, and tooling so downstream projects can focus on domain capabilities.

A production-ready foundation for developing custom Model Context Protocol (MCP) servers in TypeScript. Provides a complete layered architecture pattern, working example implementation, and comprehensive developer infrastructure to connect AI assistants with external APIs and data sources.

A streamlined foundation for building Model Context Protocol servers in Python, designed to make AI-assisted development of MCP tools easier and more efficient.

A Python-based template for creating modular command center servers using the Mondel Context Protocol that provides a structured foundation for building scalable applications with mathematical operations and modular arithmetic functions.

Enables AI agents to search, read, and explore Wikipedia articles via tools like summaries, categories, and random articles, with no API keys required.