Agentico MCP Server

The Agentico Dev MCP Server is a lightweight, opinionated wrapper around the Model Context Protocol (MCP) that turns an ordinary Node.js project into a fully‑functional MCP endpoint with minimal boilerplate. By abstracting away the low‑level protocol plumbing, it lets developers focus on writing domain logic for their tools while still exposing a standard MCP interface that any AI assistant—Claude, Gemini, or others—can consume. This approach removes the need to manually implement resource handling, tool registration, and schema validation, which are often error‑prone when done from scratch.

At its core the server exposes a single class, , that manages tool registration and lifecycle. Each custom tool extends the base class, providing a declarative schema that defines the expected parameters and a single method where business logic lives. The server automatically translates incoming MCP requests into typed calls to these methods, handles JSON‑schema validation via , and returns structured responses that the assistant can render or chain further. This pattern mirrors a classic facade design, giving developers a clean, consistent API regardless of the underlying MCP complexity.

Key capabilities include:

• Tool registration and discovery – register any number of tools by name; the server advertises them to clients through MCP’s endpoint.
• Schema‑driven validation – each tool declares its input schema; the server validates against JSON‑schema before execution, preventing runtime errors.
• Extensible response handling – responses can include text, images, or structured data; the server formats them into MCP’s content blocks.
• Versioning and metadata – specify server name, version, and description for better discoverability by assistants.

Typical use cases span from simple utilities like an echo or calculator tool to complex workflows such as database queries, file system operations, or integrations with third‑party APIs. A developer can expose a set of business services as MCP tools and let an AI assistant orchestrate them, enabling rapid prototyping of conversational interfaces or automated agents without building a full REST API.

Integration into AI workflows is seamless: once the server is running, any MCP‑compatible client can list available tools, prompt the assistant to invoke them with structured arguments, and consume the returned content. Because the server follows MCP’s standard, it plugs into existing tool‑chains and can be combined with other services—such as a prompt manager or sampling controller—without modification. The result is a modular, testable foundation for building AI‑powered applications that scale from local prototypes to production deployments.