nativeMCP

nativeMCP is a C++‑based implementation of the Model Context Protocol (MCP) that bridges AI assistants with external tools and data sources. By exposing a rich set of server capabilities—ranging from simple time utilities to network operations—it allows developers to embed domain‑specific functionality directly into conversational agents without the overhead of building a full web service.

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What problem does nativeMCP solve?

In many AI‑powered workflows, the assistant needs to interact with legacy systems or perform actions that are not part of its language model. Traditional approaches require building custom APIs, managing authentication, and maintaining separate deployment pipelines. nativeMCP eliminates this friction by providing a lightweight, standard‑compliant MCP server that can be launched from the command line and accessed via stdio. Developers can therefore turn any executable or script into a first‑class tool for an AI assistant, all while keeping the system simple and portable.

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How nativeMCP works

The architecture is split into three core components:

• MCPServer – A C++ base class that implements the MCP protocol. Sub‑classes expose specific toolsets by overriding a few pure virtual methods; no boilerplate code is required.
• servers – A collection of ready‑to‑use server implementations (e.g., , ). Each one can be launched directly, making it trivial to add new functionality by adding a new C++ class.
• host – The agent that connects to an LLM (currently via a lightweight adapter for Ollama). The host maintains a 1:1 MCP client connection and forwards tool calls from the LLM to the appropriate server.

Because communication is limited to stdio, nativeMCP can run on any platform that supports a standard console, making it ideal for integration into existing CLI workflows or lightweight Docker images.

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Key features and capabilities

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Real‑world use cases

• DevOps automation – An AI assistant can query server health, retrieve logs, or trigger redeployments by calling nativeMCP tools.
• IoT control – Devices expose MCP servers that let the assistant send commands or read sensor data over a local network.
• Enterprise data access – Secure, internal APIs can be wrapped in an MCP server, allowing the assistant to pull reports or update databases without exposing credentials.
• Rapid prototyping – Developers can spin up a new tool in minutes, test it with an LLM, and iterate without deploying a full microservice.

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Integration into AI workflows

1. Configure the host with , listing the servers and their launch parameters.
2. Start the host; it automatically loads available tools and registers them with the LLM via MCP.
3. Invoke tools from prompts; the assistant issues a standard that the host forwards to the correct server.
4. Receive structured JSON results, which can be fed back into the conversation or used to trigger downstream actions.

Because nativeMCP follows the MCP spec, any compliant client—such as Claude or Cursor—can consume its services without modification. This plug‑and‑play nature reduces integration time and keeps the assistant’s behavior deterministic.

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Unique advantages

• Pure C++ performance – No runtime overhead from a web framework; tool calls are handled in native code.
• Extensibility – The meta‑object approach means new functions can be added without changing the protocol or client code.
• Standalone – No need for a separate server process; the MCP server runs in the same console, simplifying deployment.
• Open‑source simplicity – The entire stack is available on GitHub, allowing developers to audit or modify the protocol implementation directly.

In short, nativeMCP turns any C++ application into a first‑class AI