How to Use the Clustdoc MCP in Pydantic AI

Type-safe onboarding automation for Clustdoc using Pydantic AI.

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Connect Clustdoc MCP to Pydantic AI

Create your Vinkius account to connect Clustdoc 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 Clustdoc with Pydantic AI

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Validate Clustdoc data in Pydantic AI

Every response from the server is checked against your Pydantic schemas. When the agent calls `list_client_dossiers`, it confirms the shape of the data matches your expectations. If the API returns malformed records, your agent stops immediately. This prevents the silent data corruption common in loosely typed systems.

Launch workflows with type-safe tools

Define your onboarding process with strict input parameters. The agent uses `launch_new_onboarding` only after validating the client info against your models. It then uses `send_onboarding_invitation` to finalize the setup. You get runtime guarantees that every parameter passed to Clustdoc is correct.

Inspect Clustdoc teams and templates

Retrieve structured data regarding your organization. The agent calls `list_clustdoc_teams` and `list_onboarding_templates` to map out your current operational setup. It validates the configuration with `get_workflow_configuration` before proceeding. This ensures your agent is always operating within the constraints of your defined templates.

Setup guide

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

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

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

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Common questions about Clustdoc MCP in Pydantic AI

You install the MCP support package and add the MCPToolset to your agent's toolset list. The framework validates every tool response automatically.

Because you define Pydantic models for the tools, your agent catches schema mismatches immediately. If an API field changes, your code fails loudly instead of hallucinating.

Yes, the toolset is model-agnostic. Whether you use OpenAI, Anthropic, or local models, the type validation remains consistent.

Enable verbose logging in your agent. You can see the exact input passed to the server and the validated Pydantic object returned.

The Vinkius gateway handles auth and verifies all requests. Your application records are only accessed through the validated tool interface, maintaining strict separation from other systems.

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