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
-

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//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

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