What types of MCP components can it generate?
It generates tools, resource templates, and prompt handlers based on input prompts.
@Sumedh1599
Basic information
Uses Claude or OpenAI API to convert prompt-mapped input into concrete MCP server components such as tools, resource templates, and prompt handlers.
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool 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.
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.
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.
It generates tools, resource templates, and prompt handlers based on input prompts.
Python 3.6 or higher and an API key from either Claude or OpenAI.
Yes, streaming is supported for Claude Desktop by setting `stream=True` when initializing the inferencer.
API keys can be passed directly to the constructor or set as environment variables (`CLAUDE_API_KEY` or `OPENAI_API_KEY`).
This library is currently in early development; some tests may be failing, and contributions are welcome.

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