Overview of MCP Everything

MCP Everything is a reference implementation that showcases the full breadth of features available in the Model Context Protocol (MCP). It serves as a practical playground for developers building AI assistants, illustrating how a single server can expose tools, resources, prompts, sampling, logging, and more—all through the unified MCP interface. By providing both TypeScript and Python variants that mirror each other in functionality, the project demonstrates cross‑language compatibility and encourages rapid prototyping of MCP clients.

The server addresses a common pain point for AI‑centric teams: the need to stitch together disparate services—such as calculators, image generators, and long‑running analytics—into a coherent assistant experience. Instead of writing custom adapters for each backend, developers can register tools that expose simple, well‑defined JSON schemas. The , , and tools illustrate basic command execution, while demonstrates how to integrate external language models through MCP’s sampling feature. This modularity lets developers iterate on tool logic without touching the assistant layer, fostering a clean separation of concerns.

Key capabilities include:

• Rich toolset: From arithmetic to image retrieval () and environment introspection (), each tool is documented with input schemas, return types, and optional progress notifications.
• Dynamic resources: 100 test resources are available in both plaintext and binary formats, complete with pagination, subscription support, and auto‑refresh every five seconds. This mimics real‑world data feeds such as live dashboards or file repositories.
• Prompt orchestration: Simple and complex prompts show how arguments can drive multi‑turn conversations, including image attachments, making it straightforward to prototype dialogue flows.
• LLM sampling: The tool exposes a sampling endpoint, allowing developers to plug in any LLM backend and experiment with temperature or token limits without modifying the assistant code.
• Logging and diagnostics: Random‑leveled log messages every fifteen seconds provide a realistic stream of server activity, useful for testing logging pipelines and monitoring dashboards.

In practice, developers can integrate MCP Everything into an AI workflow by pointing their assistant’s MCP client to the server’s endpoint. The assistant can then invoke any tool, fetch resources, or run prompts with a single API call. For example, a data‑analysis assistant could use to process large datasets while reporting progress, or a creative writing tool could leverage for dynamic content generation. The server’s environment‑printing tool aids debugging in CI/CD pipelines, ensuring that configuration variables are correctly propagated.

Overall, MCP Everything stands out as a comprehensive, language‑agnostic showcase that lowers the barrier to adopting MCP. Its breadth of features and clear documentation empower developers to prototype, test, and scale AI assistants with confidence, all while keeping the underlying infrastructure simple and consistent.