MCP Llm Inferencer

@Sumedh1599

Basic information

About MCP Llm Inferencer

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

MCP Llm Inferencer config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

MCP Llm Inferencer tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

What types of MCP components can it generate?

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

What are the runtime requirements?

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

Is streaming supported?

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

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`).

What is the development status?

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

Comments

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