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Fast LLM Agent MCP

This repo covers LLM, Agents concepts both theoretically and practically: LLMs, RAG, Fine Tuning, Agents, Tools, MCP, Agent Frameworks, Reference Documents, Links

Enables AI assistants to interact with Fastly's CDN API through the Model Context Protocol, allowing secure management of CDN services, caching, security settings, and performance monitoring without exposing API keys.

An MCP server for Fastmail that provides access to email, contacts, and calendars via the JMAP protocol. It enables users to search, send, and bulk-manage emails while also interacting with calendar events and address books through natural language.

Enables AI assistants to interact with Fastmail services, providing tools for comprehensive email management, contact searching, and calendar event coordination via the JMAP API. It supports advanced features like threaded conversations, attachment handling, and bulk mailbox operations through natural language.

An unofficial MCP server that enables users to manage their Fastmail accounts through natural language interactions. It provides tools to query mailboxes, retrieve email content with advanced filtering, and send messages directly through the Fastmail API.

Enables AI-powered email management through FastMail's JMAP API with features like smart email analysis, automated organization, inbox zero automation, and intelligent reply generation. Supports advanced email operations, contact management, calendar integration, and hierarchical email organization systems.

A Model Context Protocol (MCP) server that provides access to the Fastmail API, enabling AI assistants to interact with email, contacts, and calendar data.

An MCP server that integrates with FastMail's JMAP API to manage mailboxes, search for emails, and send messages. It enables users to interact with their FastMail account for tasks like reading email content and managing folders through natural language.

A TypeScript framework for building MCP servers with client session management capabilities, supporting tools definition, authentication, image content, logging, and error handling.

Fast MCP Aa72dd45

FastMCP server containerized for deployment in Google Kubernetes Engine alongside enhanced-mcp-agent

A beginner-friendly collection of MCP server implementations demonstrating calculator tools, web APIs with SSE transport, and RSS feed integration for AI agents. Includes examples using stdio and HTTP transports with validation via MCP Inspector.

A starter template for building MCP servers with FastMCP, providing testing, linting, formatting, and NPM publishing setup.

A modern Model Control Plane (MCP) project that provides a lightweight, extensible foundation for building and deploying intelligent systems that manage and expose AI/LLM capabilities through Python, FastAPI, and Docker.

A demonstration MCP server built with FastMCP that provides basic tools (addition calculator), dynamic resources (personalized greetings), and prompt templates, designed to run on Wasmer Edge.

A demonstration server showcasing MCP capabilities with basic tools including addition calculations and weather API integration for fetching city weather data.

Provides comprehensive development tools for FastMCP projects including documentation access, NPM package version management, and TypeScript type definitions retrieval. Enables developers to fetch FastMCP documentation, analyze NPM packages, and access MCP architecture information through natural language.

Enables intelligent search through FastMCP documentation using TF-IDF indexing, along with utility tools for arithmetic operations, text hashing, and web page content extraction via Jina Reader.

Enables web page scraping via Jina reader API and searching FastMCP documentation using minsearch. Supports fetching markdown content from URLs and querying indexed documentation files.

A minimal example server implementing the Model Context Protocol, providing addition and multiplication tools for learning and experimentation with MCP clients.

Educational example of an MCP server built with FastMCP, demonstrating how to expose tools, resources, and prompts for AI clients.