"How to Write Django Unit Tests" matching MCP servers:

An MCP project that orchestrates coffee orders by coordinating a Barista server and machine servers (grinder, brew unit, steamer, dispenser) through an LLM.

Enables AI agents to recruit real humans for evaluation tasks like surveys, A/B tests, and ratings on text, images, audio, and video, returning aggregated results directly into the conversation.

An MCP server that integrates a ReAct agent with built-in tools (calculator, text, unit conversion, etc.) and the AnySearch search service, enabling autonomous multi-step task execution.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

MCP executor lane for Claude Code, Codex, and Hermes Agent that offloads mechanical repo tasks like edits, tests, and commits to a cheaper or local model.

Teach any AI agent how you use an MCP server – Soft-Landing to your AI integrations

Connects MCP clients to Writ, turning saved browser workflows into callable tools. Allows running workflows, retrieving data, scheduling, and more.

Enables direct MCP-based pairing with T3 Code environments, allowing users to manage environments and projects, start and continue turns, and inspect thread history over stdio without requiring T3 Connect or a local turn CLI.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

An MCP server that provides web search capabilities through the Tavily API and weather data retrieval tools. It demonstrates how to integrate external APIs into AI applications and build agentic workflows with LangGraph.

Cryptographic identity and trust protocol for AI agents. 38 MCP tools across 8 protocol layers: Ed25519 identity, delegation chains, values compliance, signed communication, policy engine, task coordination, cross-layer integration, and agentic commerce. 264 tests passing.

A Model Context Protocol (MCP) server that demonstrates how to build and implement custom tools for Claude using the mcp-framework.

TypeScript implementation of the Model Context Protocol that standardizes how LLMs connect to data sources and tools, eliminating vendor lock-in and simplifying AI integration.

A joke/demo Model Context Protocol server that injects cringe-worthy advertisements into LLM responses, showcasing both how to build MCP servers and the risks of ad-injecting intermediaries.

MCP server for AgentMark. Exposes the full AgentMark API to AI editors: list traces and spans, manage datasets, write scores, run experiments, manage apps, deployments, environments, and annotation queues.

An MCP gateway that automatically turns Django REST Framework ViewSets into callable MCP tools with zero boilerplate, featuring a token-efficient dispatcher pattern to minimize context usage.

Enables AI agents to coordinate atomic distributed transactions across a swarm through a prepare/vote phase, unanimous commit-or-abort consensus, quorum validation, and failure recovery. It also layers in vector-clock causal stamping, LWW CRDT reconciliation, and gossip dissemination so multi-node state converges deterministically without external dependencies.

Measures and improves how findable, readable, and usable a website is to AI answer engines and autonomous AI agents through hosted AEO audit tools.