How to Use the Hasura (Instant GraphQL & REST Engine) MCP in Pydantic AI

Q: How does Pydantic AI ensure type safety with Hasura (Instant GraphQL & REST Engine)?

When your agent uses executegraphql, Pydantic AI intercepts the JSON response from Hasura. It then tries to parse that data into a Pydantic model you've defined. If there's any mismatch, it fails with an error instead of letting your agent use bad data.

Q: How do I set this up?

After installing pydantic-ai-slimmcp, you create an MCPToolset instance with the Vinkius server URL. Then you just add that toolset to your Pydantic AI Agent. It's designed to be straightforward.

Connect your Pydantic AI agent to Hasura (Instant GraphQL & REST Engine) and get type-safe, validated database responses every time.

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Connect Hasura (Instant GraphQL & REST Engine) MCP to Pydantic AI

Create your Vinkius account to connect Hasura (Instant GraphQL & REST Engine) 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 Hasura (Instant GraphQL & REST Engine) with Pydantic AI

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Claude

Perplexity

Stop Guessing What the API Returned

Use `execute_graphql` to run queries against your Hasura instance, and let Pydantic AI do the hard work. It takes the JSON response and automatically parses it into a Pydantic model you define. If the response schema ever changes or returns unexpected nulls, your agent won't silently corrupt data. Instead, it will raise a clear `ValidationError`. This forces you to handle data integrity issues head-on.

Know Your Hasura Instance is Correct

Your agent can call `get_config` and `get_version` and have the responses immediately validated against Pydantic models. This ensures your agent is always working with an accurate picture of the Hasura server's state. You can write logic that depends on this validated state. For example, your agent can refuse to run if `check_health` doesn't return a valid 'ok' status, preventing it from operating on a degraded database.

Manage Metadata with Confidence with this MCP Server

The `execute_metadata` tool lets your agent interact with the Hasura Metadata API. When paired with Pydantic AI, you can define models for your tables, permissions, and relationships. This means your agent can read the current schema and validate that it matches your expectations before attempting any changes. It's a structured way to manage database configuration, backed by the type safety of Pydantic. This MCP Server makes it simple.

Setup guide

Set up Hasura (Instant GraphQL & REST Engine) 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": {
        "hasura-instant-graphql-rest-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Hasura (Instant GraphQL & REST Engine) tools.",
)

result = await agent.run("List recent Hasura (Instant GraphQL & REST Engine) transactions")
print(result.output)

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Common questions about Hasura (Instant GraphQL & REST Engine) MCP in Pydantic AI

When your agent uses `execute_graphql`, Pydantic AI intercepts the JSON response from Hasura. It then tries to parse that data into a Pydantic model you've defined. If there's any mismatch, it fails with an error instead of letting your agent use bad data.

Yes. Pydantic AI is model-agnostic. You can use it with Gemini, Claude, Llama, or any local model to interact with this Hasura MCP server.

By default, Pydantic will ignore the extra field, so your agent's logic remains stable. If a required field is missing or has the wrong type, it will raise a `ValidationError`, which you can catch and handle in your agent's code.

After installing `pydantic-ai-slim[mcp]`, you create an `MCPToolset` instance with the Vinkius server URL. Then you just add that toolset to your Pydantic AI `Agent`. It's designed to be straightforward.

The MCP server fetches the data, but the validation happens locally in your Python environment. This means the raw GraphQL query results from your database are processed on your machine, inside your Pydantic AI agent. Pydantic's validation acts as a security check against malformed or unexpected data payloads.

Use it with your favorite AI tools

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