How to Use the Neon (Serverless PostgreSQL) MCP in Pydantic AI

Execute type-safe Neon (Serverless PostgreSQL) operations with runtime schema validation using Pydantic AI.

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Connect Neon (Serverless PostgreSQL) MCP to Pydantic AI

Create your Vinkius account to connect Neon (Serverless PostgreSQL) to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Neon (Serverless PostgreSQL) with Pydantic AI

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Perplexity

Type-Safe Database Branching

Avoid silent failures during database automation by using `create_branch` to clone your schemas safely. When your agent calls this tool, Pydantic AI validates the returned branch metadata against strict Python models at runtime. If the Neon API structure changes, your application raises a clear validation error immediately instead of passing corrupted data downstream. This guarantees that your automated database workflows remain highly predictable.

Validated Endpoint Discovery

This MCP Server exposes `list_endpoints` to fetch active PgBouncer connection strings for your branches. Connecting your application to dynamic databases requires accurate routing data, and this tool ensures you always target the correct active branch. Pydantic AI parses these endpoints into typed objects, ensuring your database drivers receive properly formatted hostnames and ports. You don't have to write custom regex or manual parsing code to handle connection strings.

Rigorous Role and Schema Auditing

Your agent uses `list_roles` to fetch security configurations directly from your Neon workspace. Auditing access control shouldn't rely on guesswork, and this tool lets you inspect active database users programmatically. The framework validates these lists against strict data schemas before your agent processes them. This ensures you can safely build automated compliance reports without worrying about malformed API payloads.

Setup guide

Set up Neon (Serverless PostgreSQL) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "neon-serverless-postgresql-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Neon (Serverless PostgreSQL) tools.",
)

result = await agent.run("List recent Neon (Serverless PostgreSQL) transactions")
print(result.output)

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Common questions about Neon (Serverless PostgreSQL) MCP in Pydantic AI

The framework intercepts every tool response, such as the output from `get_project`, and validates it against Pydantic models. If the data doesn't match the expected structure, it raises a validation error instantly.

Yes, this setup is completely model-agnostic. You can run a local model and connect it to your Neon database using the unified toolset interface.

The framework handles the API error gracefully, raising a validation or execution exception that you can catch in your Python code. This prevents your agent from continuing with a broken database state.

You run the server externally and register it in your Python script using the `MCPToolset` constructor. Your agent then auto-discovers all ten database management tools.

Your database schemas, SQL roles, and project configurations are processed entirely within a secure, ephemeral V8 sandbox. This metadata is never stored permanently or used to train the underlying language models.

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