mcp_llm_inferencer

Created By
Sumedh1599a year ago
Uses Claude or OpenAI API to convert prompt-mapped input into concrete MCP server components such as tools, resource templates, and prompt handlers.
Overview

What is mcp_llm_inferencer?

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, including tools, resource templates, and prompt handlers.

How to use mcp_llm_inferencer?

To use mcp_llm_inferencer, clone the repository, install the package, and set up your API keys for Claude or OpenAI. You can then initialize the inferencer and generate components based on your prompts.

Key features of mcp_llm_inferencer?

  • Efficient LLM call engine with retry and fallback logic.
  • Interchangeable support for Claude and OpenAI APIs.
  • Streaming support for real-time feedback from Claude Desktop.
  • Validation of generated tools and resources.
  • Structured output bundling for easier integration.

Use cases of mcp_llm_inferencer?

  1. Generating tools for data extraction from text.
  2. Creating resource templates for cloud services.
  3. Developing prompt handlers for various applications.

FAQ from mcp_llm_inferencer?

  • Can I use both Claude and OpenAI with this library?

Yes! You can seamlessly switch between Claude and OpenAI APIs based on your needs.

  • Is there a specific Python version required?

Yes, Python 3.6 or higher is required to run this library.

  • Is mcp_llm_inferencer free to use?

Yes! It is an open-source library and free for everyone.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
Sumedh1599
Star
0
Language
TypeScript
License
-

Recommend Servers

View All
Sellerguide

18 hours ago
Thiri Chord Intelligence
@BluesPrince

### Deterministic Music Theory for Claude, Cursor, and Autonomous AI Agents Large Language Models (LLMs) frequently hallucinate music theory, leading to incorrect notes, false Roman numerals, and broken voice leading. **THIRI** solves this by providing a deterministic, mathematical music-theory engine (pitch-class-set theory over ℤ/12) directly to your AI. It gives AI assistants precise, reproducible harmonic reasoning in milliseconds, allowing them to write correct musical scores, analyze progressions, and generate playable arrangements. #### 🎷 Key Features: * **Chord Analysis (`analyze_chord`):** Parse any symbol (e.g., `Cmaj7/E`, `G7#11`) to retrieve root, quality, intervals, Roman numerals, and diatonic or chromatic harmonic functions. * **Note Resolution (`resolve_chord`):** Resolve chord symbols to spelled notes (enharmonically correct), frequencies (Hz), MIDI numbers, and scale recommendations. * **Voicing Engine (`generate_voicing`):** Generate instrument-ready voicings (rootless, shell, triad, pad, drop-2, drop-3) and calculate voice-leading scores for transitions. * **Reharmonization (`reharmonize`):** Substitute progressions using classic jazz techniques, including Tritone Substitution, ii-V Insertion, Modal Interchange, Coltrane Changes, and Backdoor cadences. *Ideal for developers building AI music assistants, digital audio workstation (DAW) agents, educational theory tools, and automated composition workflows.*

an hour ago