project-01

Implements the Model Context Protocol (MCP) specification as a server, exposing a standardized interface for AI models and clients to discover and invoke capabilities through a well-defined message protocol. Uses JSON-RPC 2.0 transport layer with request/response semantics for tool registration, resource exposure, and prompt templating. Handles bidirectional communication patterns where the server can both respond to client requests and initiate server-to-client notifications.

Exposes custom tools through MCP's tool registry with JSON Schema definitions for input validation and type safety. Each tool includes a name, description, input schema (with required/optional parameters), and handler implementation. The server validates incoming tool calls against the schema before execution, ensuring type correctness and preventing malformed invocations. Supports nested object schemas, arrays, and enum constraints for rich parameter validation.

Exposes arbitrary resources (files, database records, API responses) through MCP's resource system using URI-based addressing. Resources are registered with a URI template, MIME type, and content handler. Clients request resources by URI, and the server retrieves or generates the content on demand. Supports templated URIs with variables (e.g., `file:///{path}`, `db:///{table}/{id}`) for dynamic content resolution. Resources can be text, binary, or structured data.

Provides a prompt templating system where reusable prompt templates are registered with variable placeholders and optional descriptions. Templates support variable substitution with context-aware defaults and validation. When invoked, the server resolves variables (from client input, tool outputs, or resource content) and returns the rendered prompt. Supports nested templates and conditional logic through variable references.

Implements MCP's initialization handshake where the server and client exchange capability information (supported tools, resources, prompts, sampling methods). The server advertises its capabilities through the `initialize` response, and the client declares its supported features. This enables graceful degradation when clients don't support certain MCP features (e.g., older clients without sampling support). The server can conditionally expose capabilities based on client capabilities.

Enables the MCP server to request the client (typically an AI model or agent framework) to invoke a language model for text generation, reasoning, or decision-making. The server sends a sampling request with a prompt, model parameters (temperature, max_tokens, stop sequences), and optional system context. The client handles the actual model invocation and returns the generated text. This reverses the typical client-server relationship, allowing servers to leverage AI capabilities without embedding a model.

Abstracts the underlying transport mechanism, supporting multiple protocols for client-server communication: stdio (for local processes), HTTP (for network clients), and WebSocket (for real-time bidirectional communication). The server implementation handles protocol-specific details (serialization, connection management, error handling) while exposing a unified MCP message interface. Clients can connect via their preferred transport without the server needing to know the details.