How to Use the Nile (PostgreSQL for Multi-Tenant Apps) MCP in Pydantic AI

Q: How do I register Nile (PostgreSQL for Multi-Tenant Apps) tools in Pydantic AI?

Install pydantic-ai-slimmcp and use MCPToolset with your Vinkius server URL. Pass this toolset directly to your Agent constructor to automatically register the database management tools.

Q: What happens if Nile (PostgreSQL for Multi-Tenant Apps) returns unexpected database metrics in Pydantic AI?

Pydantic AI validates the response from getmetrics against its internal models at runtime. If the database returns malformed or missing performance data, the agent halts and throws a validation error.

Q: Can I use Pydantic AI to automate tenant onboarding with this MCP Server?

Yes. Your agent can call createtenant to provision a new virtual tenant boundary. The framework ensures the tenant ID matches your required string format before sending the request to Nile.

Q: How does the toolset handle connection pooling and database routing?

The agent uses listdatabases and getdatabase to fetch connection configurations. Once validated, your Python application uses these endpoints to route SQL queries to the correct tenant database.

Build type-safe Python agents that manage Nile multi-tenant databases with strict runtime validation using Pydantic AI.

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Create your Vinkius account to connect Nile (PostgreSQL for Multi-Tenant Apps) 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 Nile (PostgreSQL for Multi-Tenant Apps) with Pydantic AI

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Type-safe database tenant provisioning

`create_tenant` provisions a highly isolated virtual tenant boundary inside your Nile database. This tool runs within Pydantic AI's type-safe execution loop, ensuring that tenant names and configurations strictly match your schema before execution using the MCP Server. If the database returns unexpected data, the framework raises a loud validation error immediately instead of failing silently. This runtime validation prevents malformed tenant IDs or corrupted metadata from entering your application logic.

Audit database infrastructure using the MCP Server

`list_databases` identifies root connectivity endpoints necessary for querying physical data, and `get_database` gets configuration and state details for a specific Nile database. These tools allow your Python agent to map out physical endpoints with absolute type safety. The framework validates the returned database configurations against strict Pydantic models. This ensures your agent always receives clean, predictable connection strings and state details before attempting to run queries.

Track database strain with typed metrics

`get_metrics` pulls exact operational performance numbers tracking database strain. This tool returns precise CPU utilization, query counts, and connection statistics from your Nile instance. Pydantic AI validates these incoming metrics against strict numeric schemas. Your agent can safely evaluate these performance numbers to trigger alerts, knowing that the data has not been corrupted during transit.

Setup guide

Set up Nile (PostgreSQL for Multi-Tenant Apps) 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": {
        "nile-postgresql-for-multi-tenant-apps-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Nile (PostgreSQL for Multi-Tenant Apps) tools.",
)

result = await agent.run("List recent Nile (PostgreSQL for Multi-Tenant Apps) transactions")
print(result.output)

Get your connection token →

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Common questions about Nile (PostgreSQL for Multi-Tenant Apps) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use `MCPToolset` with your Vinkius server URL. Pass this toolset directly to your `Agent` constructor to automatically register the database management tools.

Pydantic AI validates the response from `get_metrics` against its internal models at runtime. If the database returns malformed or missing performance data, the agent halts and throws a validation error.

Yes. Your agent can call `create_tenant` to provision a new virtual tenant boundary. The framework ensures the tenant ID matches your required string format before sending the request to Nile.

The agent uses `list_databases` and `get_database` to fetch connection configurations. Once validated, your Python application uses these endpoints to route SQL queries to the correct tenant database.

Yes. The MCP server only handles administrative metadata such as database metrics, tenant list arrays, and user access lists. Vinkius runs the server in an ephemeral sandbox, keeping your master credentials hidden from the agent.

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