mcp_llm_inferencer
About
Uses Claude or OpenAI API to convert prompt-mapped input into concrete MCP server components such as tools, resource templates, and prompt handlers.
Explore
- LLM Call Engine: Efficiently calls LLMs with built-in retry and fallback logic to ensure reliable responses.
- Interchangeable Claude & OpenAI Support: Seamlessly switch between Claude and OpenAI APIs based on your preference or availability.
- Streaming Support for Claude Desktop: Stream responses directly from Claude Desktop, providing real-time feedback.
- Tool and Resource Response Validation: Ensures that the generated tools and resources meet predefined criteria before deployment.
- Structured Output Bundling: Organizes output into structured bundles per component, simplifying integration and use.
- Python 3.6 or higher
- An API key from Claude or OpenAI
1. Clone the repository:
git clone https://github.com/your-repo/mcp_llm_inferencer.git
cd mcp_llm_inferencer
2. Install the package using pip:
pip install .
3. Set up your API keys as environment variables:
- For Claude:
export CLAUDE_API_KEY='your-claude-api-key'
- For OpenAI:
export OPENAI_API_KEY='your-openai-api-key'
components = inferencer.generate_components("Generate a tool for sentiment analysis.")
Introduction
The mcp_llm_inferencer is an open-source library designed to leverage the power of Large Language Models (LLMs) such as Claude and OpenAI's GPT to convert prompt-mapped inputs into concrete components for MCP servers. These components include tools, resource templates, and prompt handlers, making it a versatile tool for developers working with MCP server environments.
Features
- LLM Call Engine: Efficiently calls LLMs with built-in retry and fallback logic to ensure reliable responses.
- Interchangeable Claude & OpenAI Support: Seamlessly switch between Claude and OpenAI APIs based on your preference or availability.
- Streaming Support for Claude Desktop: Stream responses directly from Claude Desktop, providing real-time feedback.
- Tool and Resource Response Validation: Ensures that the generated tools and resources meet predefined criteria before deployment.
- Structured Output Bundling: Organizes output into structured bundles per component, simplifying integration and use.
Installation Instructions
Prerequisites
- Python 3.6 or higher - An API key from Claude or OpenAIInstalling mcp_llm_inferencer
1. Clone the repository:
git clone https://github.com/your-repo/mcp_llm_inferencer.git
cd mcp_llm_inferencer
2. Install the package using pip:
pip install .
3. Set up your API keys as environment variables:
- For Claude:
export CLAUDE_API_KEY='your-claude-api-key'
- For OpenAI:
export OPENAI_API_KEY='your-openai-api-key'
Usage Examples
Basic Example
Here is a simple example demonstrating how to use mcp_llm_inferencer with the OpenAI API:
```python
from mcp_llm_inferencer import MCPInferencer
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