Simple data retrieval for AI with unmatched control, precision, and speed.
Documentation: docs.toolfront.ai
Install with pip or your favorite PyPI package manager.
pip install toolfrontfrom toolfront import Database
db = Database("postgres://user:pass@localhost:5432/mydb")
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?", model="openai:gpt-4o", 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.
from toolfront import API
api = API("http://localhost:8000/openapi.json")
# Returns a list of integers
answer: list[int] = api.ask("Get the last 5 order IDs for user_id=42", model="anthropic:claude-3-5-sonnet")
# >>> [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.
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")
# Returns a structured Pydantic object
answer: CompanyReport = doc.ask("Extract the key company information from this report", model="google:gemini-pro")
# >>> 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.
{
"mcpServers": {
"toolfront": {
"command": "uvx",
"args": [
"toolfront[snowflake]",
"snowflake://user:pass@account/warehouse/database"
]
}
}
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This project is licensed under the terms of the MIT license.