Mcpcalculator

Overview

The MCP Calculator is a lightweight, Go‑based Model Context Protocol (MCP) server that exposes two simple yet illustrative tools—addition and greeting—through a fully compliant JSON‑RPC 2.0 interface. By implementing the MCP specification end‑to‑end, it demonstrates how an AI assistant can discover, negotiate capabilities, and invoke external services in a secure, typed manner. The server runs on a single port (default 8080) and requires only Go 1.21+, making it trivial to integrate into existing Go ecosystems or containerized deployments.

Problem Solved

Many AI assistants need to delegate domain‑specific logic (e.g., calculations, data retrieval) to external services while maintaining a strict contract on inputs and outputs. Traditional REST or gRPC approaches can be verbose or lack the dynamic discovery that AI agents require. MCP bridges this gap by allowing an assistant to query a server’s capabilities, receive typed tool definitions, and execute them with guaranteed response schemas. The Calculator MCP shows how a minimal service can be exposed without boilerplate, yet still adhere to the protocol’s security and error‑handling guidelines.

Core Value for Developers

For developers building AI‑enabled applications, the Calculator MCP offers a ready‑made example of:

• Tool registration: Tools are defined once and made discoverable through the MCP initialization handshake.
• Resource handling: The greeting endpoint illustrates a read‑only resource that can be queried without side effects.
• Capability negotiation: Clients learn what operations are available before attempting calls, reducing runtime errors.
• Standardized error reporting: All failures surface through JSON‑RPC error objects, simplifying client logic.

Because the implementation is pure Go and follows the official spec verbatim, it can serve as a reference for building more complex MCP services—whether they perform financial calculations, access databases, or orchestrate external APIs.

Key Features Explained

• Full JSON‑RPC 2.0 compliance: Every request and response follows the official specification, ensuring interoperability with any MCP‑aware client.
• Tool execution protocol: The server registers the tool, accepting two numeric parameters and returning their sum. This demonstrates parameter validation and result serialization.
• Resource access protocol: The resource accepts a name string and returns a friendly message, showing how read‑only data can be exposed.
• Capability negotiation: During the initialization phase, the server advertises its supported methods and data types, allowing clients to adapt dynamically.
• Robust error handling: Invalid parameters or internal failures trigger standardized JSON‑RPC errors, providing clear diagnostics for developers.

Real‑World Use Cases

• Chatbot Calculations: An AI assistant can offload arithmetic to the Calculator MCP, freeing up compute resources and keeping the assistant lightweight.
• Personalized Greetings: Integrate the greeting resource into a virtual concierge that tailors responses based on user identity.
• Education Platforms: Use the calculator tool to demonstrate programming concepts or solve homework problems within an AI tutor.
• Microservice Testing: Serve as a mock MCP server during integration tests, ensuring that client code correctly handles tool discovery and invocation.

Integration into AI Workflows

An MCP‑aware assistant first initiates a connection to the server, receives its capabilities, and caches tool definitions. When a user request requires computation or data retrieval, the assistant constructs a JSON‑RPC request with the appropriate method name and parameters. The server validates the input, executes the tool or resource logic, and returns a typed result—all within milliseconds. The assistant can then embed this result directly into its response, maintaining the illusion of a single, cohesive system while leveraging external services.

Unique Advantages

• Protocol‑First Design: By adhering strictly to MCP, the server guarantees that any compliant client can interact without custom adapters.
• Security by Design: Explicit invocation, data privacy guarantees, and validated tool execution reduce the attack surface compared to ad‑hoc APIs.
• Extensibility: Adding new tools or resources is a matter of defining the method signature and implementation; the MCP machinery handles registration automatically.

In summary, the MCP Calculator is more than a toy service—it is a practical showcase of how Model Context Protocol can streamline AI‑assistant integrations, enforce type safety, and provide secure, discoverable tooling for developers.