homemade-mcp 0.1.0

Homemade-MCP is a tiny Python library that gives you one standard input/output format for multiple AI providers.

Homemade-MCP

Homemade-MCP is a tiny Python library that gives you one standard input/output format for multiple AI providers (OpenAI GPT, Google Gemini, Mistral OCR).

✨ Features:

• Unified request/response schema (no more provider-specific formats).

• LLM & OCR support: text, text+image, and documents.

• Structured outputs: enforce JSON schema across models.

• Thinking control (reasoning budget / effort) when supported.

• Search grounding (Gemini’s Google Search integration).

• Batch processing:

  • “Concurrent” mode → fast async fan-out.
  • “Native” batch (OpenAI & Gemini) → cheaper large jobs with JSONL.

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Why?

Each provider (OpenAI, Gemini, Mistral) exposes different SDKs, request shapes, and batch/structured-output quirks. Homemade-MCP acts as a “universal socket”: one entry point, one output format. Swap providers or models without rewriting your code.

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Installation

pip install -e .

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Quick start

from homemade_mcp.client import LLMClient
from homemade_mcp.schemas import StdRequest, LLMInput, PDFDoc

client = LLMClient({
  "openai": {"api_key": "..."},
  "gemini": {"api_key": "..."},
  "mistral_ocr": {"api_key": "..."},
})

req = StdRequest(
    provider="openai",
    model="gpt-5",
    task="llm",
    input=LLMInput(
        blocks=["Extract title & price: Widget X costs 12.99"],
        json_schema={"type":"object","properties":{"title":{"type":"string"},"price":{"type":"number"}},"required":["title","price"]},
        thinking="low"
    )
)

resp = await client.run(req)
print(resp.content)  # {'title': 'Widget X', 'price': 12.99}

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Roadmap

• Add batch job polling helpers (OpenAI & Gemini).
• Extend OCR adapter (PDFs, multi-page).
• PDF rasterization helper (images for LLM vision) [optional extra dep].
• Add more providers (Anthropic, Cohere, Claude OCR).
• CLI tool for quick testing.

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Contributing

Pull requests and issues welcome!

• Keep code simple (no heavy abstractions).
• Follow PEP8.
• Add examples if you add features.

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