TianGong AI MCP Server

The Tiangong AI Model Context Protocol (MCP) Server is a lightweight, HTTP‑based service that implements the MCP specification for streaming interactions. It resolves a common pain point for developers: exposing custom AI tools, resources, and prompts to large‑language‑model assistants in a standardized way. By running the server locally or on any cloud instance, teams can quickly bind their proprietary data sources (databases, APIs, file systems) to an AI assistant without reinventing the networking layer.

At its core, the server handles streamable HTTP requests that carry MCP messages. Each request can include a tool invocation, resource lookup, or prompt definition, and the server streams back incremental responses. This streaming capability is essential for building responsive user experiences where an assistant can start delivering results before the entire payload arrives. It also simplifies integration with conversational agents that expect real‑time feedback, such as chat interfaces or voice assistants.

Key features include:

• Tool registration and discovery – Developers can expose any function or API as a callable tool. The server advertises these tools to the assistant, enabling dynamic invocation without hard‑coding endpoints.
• Resource management – Static assets or contextual data can be served as resources, allowing assistants to retrieve files or documents on demand.
• Prompt templating – The server supports prompt templates that can be populated with runtime data, making it easier to generate context‑aware prompts for the model.
• Sampling configuration – Fine‑tune generation parameters (temperature, top‑k, etc.) directly through the MCP interface, giving developers granular control over output style and creativity.

Typical use cases span from internal knowledge bases (querying company documentation) to automation pipelines (triggering CI/CD jobs or database updates). A data scientist might expose a model inference endpoint as a tool, while an operations engineer could provide log‑search resources. In each scenario, the MCP server acts as a bridge that translates standard HTTP calls into structured tool invocations understood by the assistant.

What sets Tiangong AI MCP apart is its emphasis on streamability and simplicity. The server’s single‑command launch, combined with the optional Inspector UI for debugging, allows teams to iterate rapidly. Because it follows the MCP standard, any assistant that implements the protocol can consume the services without custom adapters. This plug‑and‑play nature makes it an ideal foundation for building AI‑powered applications that require tight integration with existing infrastructure.