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Centralizes the discovery, comparison, and implementation of Model Context Protocol (MCP) servers, ranked by community popularity.
Connects saved Noverload content to AI assistants, enabling advanced search, content synthesis, and intelligent token management through the Model Context Protocol (MCP).
Retrieves Drupal module information from Drupal.org for enhanced development workflows.
Enables AI assistants to understand and generate GPAC command examples by providing intelligent access to the GPAC multimedia framework test suite.
Provides foundational example code for building Model Context Protocol (MCP) servers using FastAPI.
Manages document ingestion, chunking, semantic search, and note management.
Demonstrates the Model Context Protocol (MCP) through a simple client-server greeting tool utilizing standard input and output for communication.
Generates multi-language greetings and provides server capabilities through an HTTP-based Model Context Protocol server.
Integrates Confluence with the Model Context Protocol to enhance context awareness and streamline documentation workflows.
Access real-time information about U.S. National Parks, including park details, alerts, activities, and facilities via the National Park Service API.
Enables large language models to fetch, analyze, and summarize academic research papers from multiple trusted sources in real-time.
Connects AI assistants and Large Language Models to GitHub, Confluence, and Databricks environments via the Model Context Protocol.
Maintains project documentation and progress logs consistently across AI agents.
Provides an example implementation of a Message Communication Protocol (MCP) server using the FastMCP framework.
Demonstrates integrating FastAPI and FastMCP to serve LLM-callable tools via the MCP protocol.
Provides a Model Context Protocol (MCP) server for processing local documents and answering questions based on their content.
Provides a simple, experimental server for integrating tools with local AI chat agents.
Provides slides and materials for a workshop on deploying applications to Kubernetes.
Enables local middleware for seamless communication between LLM-based tools on Windows OS.
Empowers AI assistants with intelligent access to ML textbook content for creating accurate, source-grounded documentation.
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