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MCP Llm Inferencer

@Sumedh1599

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基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

Sumedh1599

MCP Llm Inferencer について

Uses Claude or OpenAI API to convert prompt-mapped input into concrete MCP server components such as tools, resource templates, and prompt handlers.

#open-source#machine-learning#library#ai#llm-call-engine-with#supports-claude-and#streaming-support-fo#tool-and-resource-re#structured-output-bu

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MCP Llm Inferencer のツール

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概要

What is mcp_llm_inferencer?

mcp_llm_inferencer is an open-source library that uses Large Language Models (LLMs) like Claude and OpenAI’s GPT to convert prompt-mapped inputs into concrete components for MCP servers, such as tools, resource templates, and prompt handlers. It is designed for developers working with MCP server environments.

How to use mcp_llm_inferencer?

Clone the repository, install with pip, and set API keys as environment variables (CLAUDE_API_KEY or OPENAI_API_KEY). Initialize the MCPInferencer class with the desired API type ('claude' or 'openai') and call generate_components(prompt) to produce MCP components. Optionally enable streaming by setting stream=True when using Claude.

Key features of mcp_llm_inferencer

  • LLM call engine with built-in retry and fallback logic.
  • Interchangeable support for Claude and OpenAI APIs.
  • Streaming support for Claude Desktop responses.
  • Tool and resource response validation before deployment.
  • Structured output bundling per component.

Use cases of mcp_llm_inferencer

  • Automatically generate MCP tools from natural language prompts.
  • Create resource templates (e.g., for an S3 bucket) via LLM prompts.
  • Build prompt handlers for MCP servers without manual coding.
  • Prototype MCP server components using either Claude or OpenAI.

よくある質問

1

What types of MCP components can it generate?

It generates tools, resource templates, and prompt handlers based on input prompts.

2

What are the runtime requirements?

Python 3.6 or higher and an API key from either Claude or OpenAI.

3

Is streaming supported?

Yes, streaming is supported for Claude Desktop by setting `stream=True` when initializing the inferencer.

4

How are API keys managed?

API keys can be passed directly to the constructor or set as environment variables (`CLAUDE_API_KEY` or `OPENAI_API_KEY`).

5

What is the development status?

This library is currently in early development; some tests may be failing, and contributions are welcome.

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