mcp-run-python

# SQL Generation Example demonstrating how to use Pydantic AI to generate SQL queries based on user input. Demonstrates: - [dynamic system prompt](../agents.md#system-prompts) - [structured `output_type`](../output.md#structured-output) - [output validation](../output.md#output-validator-functions) - [agent dependencies](../dependencies.md) ## Running the Example The resulting SQL is validated by running it as an `EXPLAIN` query on PostgreSQL. To run the example, you first need to run PostgreSQL, e.g. via Docker: ```bash docker run --rm -e POSTGRES_PASSWORD=postgres -p 54320:5432 postgres ``` _(we run postgres on port `54320` to avoid conflicts with any other postgres instances you may have running)_ With [dependencies installed and environment variables set](./setup.md#usage), run: ```bash python/uv-run -m pydantic_ai_examples.sql_gen ``` or to use a custom prompt: ```bash python/uv-run -m pydantic_ai_examples.sql_gen "find me errors" ``` This model uses `gemini-1.5-flash` by default since Gemini is good at single shot queries of this kind. ## Example Code ```snippet {path="/examples/pydantic_ai_examples/sql_gen.py"}```