MCP-PoC Server

MCP‑PoC: A TypeScript Proof‑of‑Concept Server for the Model Context Protocol
The MCP‑PoC server demonstrates how an external service can expose a set of resources, tools, prompts, and sampling strategies to AI assistants through the Model Context Protocol (MCP). By implementing the MCP specification in TypeScript, this server serves as a lightweight reference implementation that developers can run locally or deploy on a cloud environment to test and iterate on AI‑assistant integrations.

Solving the Integration Gap
Developers building conversational agents often need to connect an assistant like Claude to external data stores, APIs, or custom logic. Without a standardized protocol, each integration requires bespoke adapters and can quickly become brittle. MCP‑PoC solves this problem by providing a ready‑made server that follows the MCP contract, allowing assistants to discover available capabilities and invoke them with a uniform request/response format. This eliminates the need for custom glue code, reduces integration time, and ensures consistent error handling across different services.

Core Functionality and Value
At its heart, MCP‑PoC offers a sandboxed environment where developers can register resources (e.g., database connections, file systems), expose tools (functions that perform computations or fetch data), define reusable prompts, and configure sampling parameters for text generation. The server’s TypeScript implementation gives developers type safety, IDE support, and the ability to extend or modify behavior with minimal friction. By exposing these capabilities through a well‑defined API, assistants can perform complex tasks—such as retrieving the latest stock price or summarizing a document—without embedding business logic directly into the assistant’s codebase.

Key Features Explained

• Resource Management: Declarative definitions of external systems (e.g., REST APIs, databases) that can be referenced by tools.
• Tool Execution: JSON‑based invocation of server functions with automatic input validation and result serialization.
• Prompt Templates: Pre‑configured prompt snippets that assistants can inject into conversations, promoting consistency and reuse.
• Sampling Controls: Fine‑grained parameters (temperature, top‑p, max tokens) that assistants can tweak on the fly to balance creativity and determinism.
• TypeScript Support: Strong typing for inputs/outputs, making it easier to catch errors during development and improving documentation generation.

Real‑World Use Cases

• Data Retrieval: An assistant can query a database through an MCP tool to fetch customer details, then format the response for the user.
• Third‑Party API Integration: External services such as weather or payment gateways can be exposed as tools, letting the assistant orchestrate calls without hardcoding credentials.
• Custom Analytics: Developers can implement domain‑specific analytics functions (e.g., sentiment analysis) and expose them via MCP, enabling the assistant to provide insights on demand.
• Rapid Prototyping: Teams can spin up the PoC server locally to prototype new capabilities before moving them into production environments.

Integration with AI Workflows
MCP‑PoC fits seamlessly into existing assistant pipelines. Once the server is running, an AI client can query its endpoint to discover available tools and prompts. During a conversation, the assistant can request tool execution by sending a structured JSON payload; the server processes the request, performs the operation, and returns the result. This decouples business logic from the assistant’s core model, allowing developers to iterate on tools independently while keeping the conversational flow intact. The server’s TypeScript foundation also makes it straightforward to generate client SDKs, further simplifying integration for front‑end or back‑end developers.

Unique Advantages
Because it is a proof‑of‑concept written in TypeScript, MCP‑PoC offers an approachable entry point for teams already familiar with JavaScript/Node ecosystems. Its modular design encourages experimentation: developers can add new resources or tools without touching the core server code. Moreover, by adhering strictly to the MCP spec, it guarantees compatibility with any future MCP‑compliant assistant, ensuring that the integration remains robust as protocols evolve.