EasyMcp – A Rapid‑Build Model Context Protocol Server

EasyMcp is a lightweight, highly extensible framework that turns any Python project into a fully‑featured MCP (Model Context Protocol) server. It eliminates the boilerplate typically required to expose custom tools, prompts, and data sources to AI assistants such as Claude. By supporting all major MCP transmission modes—STDIO, Server‑Sent Events (SSE), and streamable HTTP—the framework gives developers the freedom to choose the most suitable transport for their environment, whether it’s a local CLI experiment or a production‑grade microservice.

What Problem Does EasyMcp Solve?

Modern AI assistants rely on external knowledge bases, databases, and custom logic to answer complex queries. Building an MCP server from scratch involves writing repetitive handler code, configuring routing, and managing multiple communication protocols. EasyMcp removes these hurdles by providing a clear directory structure ( for tools, for settings) and a minimal set of conventions. Developers can add new capabilities with a single class that inherits from , define its schema, and implement the business logic—all without touching networking code.

Core Features & Capabilities

• Protocol‑agnostic transport – seamlessly switch between STDIO, SSE, or streamable HTTP with a single flag or configuration change.
• Automatic tool discovery – any class placed in and exported in is registered as an MCP tool, eliminating manual registration.
• Schema‑driven input validation – each tool exposes a JSON schema via , ensuring that clients receive precise instructions on required parameters.
• Asynchronous execution – is an async method, allowing I/O‑bound operations (database queries, API calls) to run concurrently without blocking the server.
• Environment‑based configuration – sensitive settings such as database credentials are read from , keeping secrets out of source control.

Real‑World Use Cases

• Database Interaction – A tool that queries a MySQL or PostgreSQL database can be added with just a few lines, enabling AI assistants to fetch live data for reports or dashboards.
• Custom Business Logic – Whether it’s a recommendation engine, a fraud‑detection routine, or an internal workflow trigger, developers can expose any Python function as an MCP tool.
• Hybrid Workflows – Combine local tools (STDIO) with cloud‑hosted services (SSE or HTTP) to create a hybrid pipeline where sensitive data stays on-premises while leveraging external APIs for heavy lifting.

Integration with AI Workflows

AI assistants consume MCP servers by declaring them in a configuration JSON. EasyMcp’s straightforward URL patterns ( for streamable HTTP, for SSE) make it trivial to point a client such as Claude’s or . Once registered, the assistant can invoke any exposed tool by name, passing a JSON payload that matches the declared schema. The server responds with a stream of objects, allowing incremental output that feels natural in conversational contexts.

Unique Advantages

• Zero‑config “just‑works” – No need for complex Docker setups or Kubernetes manifests; a single command launches the server in any supported mode.
• Extensibility without friction – Adding a new tool is as simple as creating a file in ; the framework handles registration and routing automatically.
• Consistent developer experience – The same handler pattern applies across all protocols, so developers can focus on business logic rather than transport details.

In summary, EasyMcp provides a plug‑and‑play MCP server that scales from local experiments to production deployments, empowering developers to expose rich, custom functionality to AI assistants with minimal effort.