How to Use the Coalesce MCP in Pydantic AI

Build type-safe data agents with Pydantic AI to validate every Coalesce pipeline action and run status at runtime.

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Connect Coalesce MCP to Pydantic AI

Create your Vinkius account to connect Coalesce 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 Coalesce with Pydantic AI

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Type-safe Snowflake pipeline execution with Pydantic AI

The Coalesce MCP Server provides type-safe access to your Snowflake pipelines through Pydantic AI validation. When your agent calls `trigger_run` or `trigger_job`, Pydantic AI validates the environment and job IDs against strict schemas before sending the request. If the agent attempts to pass an invalid string format, the SDK blocks the call immediately. This guarantees that your Coalesce platform only receives perfectly structured API requests.

Fail loudly on unexpected metadata changes

This MCP Server exposes metadata from your active data pipelines directly to your validation models. When your Pydantic AI agent queries `list_nodes` or `get_environment` in Coalesce, the returned JSON is parsed directly into strict models. If a Coalesce node configuration changes unexpectedly, the Pydantic AI system throws a validation error instead of passing bad data to your model. This prevents silent failures in your data pipeline automation.

Predictable job monitoring and status checks

The Coalesce MCP Server delivers structured status updates for tracking your data transformations. Your agent calls `get_run_status` and parses the progress state into typed Python objects. You can confidently write logic based on the status field, knowing the framework guarantees its structure. If `get_job_details` returns an unexpected status code, your Pydantic AI code catches it instantly.

Setup guide

Set up Coalesce 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": {
        "coalesce-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Coalesce transactions")
print(result.output)

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Common questions about Coalesce MCP in Pydantic AI

The framework wraps the server tools in typed schemas. When tools like `get_run_status` return data, it is validated against Pydantic models to ensure absolute type safety.

Yes. You can define custom models that restrict the inputs for `trigger_job` to specific environment names, stopping invalid runs before they execute.

The SDK raises a validation error immediately. This stops the agent from operating on dirty data and alerts you to schema changes in `list_nodes`.

Yes. You can use this server with OpenAI, Anthropic, or local models. Pydantic AI handles the tool schema generation for whichever model you choose.

Your tokens are stored as encrypted environment variables in the Vinkius hosting layer. The Pydantic AI client only interacts with the validated data payloads, never exposing your credentials.

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