How to Use the Document360 MCP in Pydantic AI

Use Pydantic AI to build type-safe documentation agents with the Document360 MCP Server.

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Connect Document360 MCP to Pydantic AI

Create your Vinkius account to connect Document360 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 Document360 with Pydantic AI

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Type-safe project and category management

Every response from `list_projects` and `list_categories` is validated against your Pydantic models at runtime. If the API returns unexpected data, the agent fails before it can process bad information. This ensures your agent's internal state remains consistent with the actual structure of your knowledge base. It eliminates the risk of hallucinated fields or silent data corruption.

Reliable article retrieval for Pydantic AI

Use `get_article` to pull specific documentation into your agent. Because the output is validated, you know exactly what format the content takes before it hits your model. Call `list_articles` to iterate through categories with full schema enforcement. It makes building automated documentation pipelines much more predictable.

Validated search and team data

The `search` tool returns results that your agent can immediately trust. If the data structure deviates from your expected model, the agent catches it instantly. Use `list_team` to pull contributor data into your Pydantic models. It creates a robust link between your documentation and the team members who own it.

Setup guide

Set up Document360 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": {
        "document360-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Document360 tools.",
)

result = await agent.run("List recent Document360 transactions")
print(result.output)

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Common questions about Document360 MCP in Pydantic AI

Pydantic AI provides runtime validation for all tool responses. This prevents your agent from acting on malformed data returned by the Document360 API.

Because the server is integrated with Pydantic, any API failure or unexpected response triggers a validation error. You can catch these in your agent logic to manage retries.

No. The server acts as a pass-through. It does not cache or store your Document360 data, ensuring your information remains private.

Yes. The server is model-agnostic. You can use local models or external LLMs while still enforcing type safety on all incoming data.

Security is managed by a single endpoint token. You pass this to the server configuration, ensuring all traffic is authenticated and private.

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