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ToolFront MCP Server

by kruskal-labs
GitHub
Python
MIT License
293
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Simple data retrieval for AI with unmatched control, precision, and speed.

Documentation: docs.toolfront.ai

Installation

Install with pip or your favorite PyPI package manager.

pip install toolfront

Example 1: Text2SQL with ChatGPT

from toolfront import Database db = Database("postgres://user:pass@localhost:5432/mydb", model="openai:gpt-4o") context = "We're an e-commerce company. Sales data is in the `cust_orders` table." # Returns a string answer = db.ask("What's our best-selling product?", context=context) # >>> "Wireless Headphones Pro"

Note: For databases, install with PyPI extras, e.g.: pip install "toolfront[postgres]". See the documentation for the complete list of 10+ databases.

Example 2: API retrieval with Claude

from toolfront import API api = API("http://localhost:8000/openapi.json", model="anthropic:claude-3-5-sonnet") # Returns a list of integers answer: list[int] = api.ask("Get the last 5 order IDs for user_id=42") # >>> [1001, 998, 987, 976, 965]

Note: ToolFront supports any API with an OpenAPI (formerly Swagger) specification. Most common APIs like Slack, Discord, and GitHub have OpenAPI specs. See the documentation for more details.

Example 3: Document information extraction with Gemini

from toolfront import Document from pydantic import BaseModel, Field class CompanyReport(BaseModel): company_name: str = Field(..., description="Name of the company") revenue: int | float = Field(..., description="Annual revenue in USD") is_profitable: bool = Field(..., description="Whether the company is profitable") doc = Document("/path/annual_report.pdf", model="google:gemini-pro") # Returns a structured Pydantic object answer: CompanyReport = doc.ask("Extract the key company information from this report") # >>> CompanyReport(company_name="TechCorp Inc.", revenue=2500000, is_profitable=True)

Note: ToolFront supports OpenAI, Anthropic, Google, xAI, and 14+ AI model providers. See the documentation for the complete list.

Example 4: Snowflake MCP Server

{ "mcpServers": { "toolfront": { "command": "uvx", "args": [ "toolfront[snowflake]", "snowflake://user:pass@account/warehouse/database" ] } } }

Community & Contributing

License

This project is licensed under the terms of the MIT license.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Securely connects AI agents to multiple databases simultaneously while enabling collaborative learning from team query patterns, all while keeping data private by running locally.

  1. Example 1: Text2SQL with ChatGPT
    1. Example 2: API retrieval with Claude
      1. Example 3: Document information extraction with Gemini
        1. Example 4: Snowflake MCP Server
          1. Community & Contributing
            1. License

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