MCP Bone Server

MCP Bone is an online MCP (Model Context Protocol) server that centralizes tool discovery, registration, and invocation for AI assistants.
Developers often face the challenge of wiring together disparate services—databases, APIs, custom logic—into a cohesive set of function‑calling tools that an LLM can invoke. MCP Bone removes this friction by offering a single, well‑defined endpoint where any number of external MCP servers can register their capabilities. Once registered, those tools become instantly available to any client that connects to MCP Bone, eliminating the need for manual configuration or hard‑coded URLs.

At its core, MCP Bone exposes three key interfaces:

• Tool registration – External MCP servers send a JSON or XML description of their available tools, including parameters and return types. The server validates and stores these definitions in a searchable registry.
• Tool discovery – Clients query the registry to retrieve a consolidated list of all registered tools. The response is delivered in standard MCP JSON, ready for consumption by LLMs that support function calling.
• Tool invocation – When a client calls a tool, MCP Bone forwards the request to the originating server, aggregates the response, and returns it in a uniform format. This guarantees consistent error handling and data normalization across heterogeneous backends.

The real value for developers lies in the seamless integration with AI workflows. A typical use case involves a chatbot that needs to book flights, query inventory, or run custom analytics. Instead of embedding each service’s API directly into the bot code, a developer registers those services with MCP Bone once. The LLM then receives a single, curated list of tools and can invoke any of them through the standard function‑calling syntax. This modularity accelerates iteration, reduces boilerplate, and makes it trivial to swap or upgrade individual services without touching the assistant’s logic.

MCP Bone also offers a parser SDK that turns arbitrary LLM completions—especially those from models that do not yet support native function calling—into structured tool calls. By extracting JSON, XML, or plain‑text tool definitions from the model’s output, the SDK ensures that even legacy models can participate in a modern, tool‑centric workflow. This capability is particularly useful during migration phases or when experimenting with different LLMs.

In summary, MCP Bone acts as a tool‑hub for AI assistants: it solves the problem of scattered, manually wired services; provides a standardized discovery and invocation interface; supports both native and legacy LLMs through its parsing SDK; and ultimately empowers developers to build richer, more dynamic AI applications with minimal friction.