How to Use the Flow MCP in Pydantic AI

Q: How do I connect the Flow MCP Server to Pydantic AI?

Install pydantic-ai-slimmcp and use the MCPToolset constructor with your Vinkius HTTP URL. Pass the toolset instance directly into your Agent definition.

Q: Does Pydantic AI validate the Flow API responses?

Yes, the framework validates the output of tools like listtasks against its internal schemas. This guarantees your agent receives clean, structured data every time.

Build type-safe project workflows with Pydantic AI and Flow using runtime schema validation.

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Connect Flow MCP to Pydantic AI

Create your Vinkius account to connect Flow 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 Flow with Pydantic AI

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Strict task validation with Pydantic AI

This MCP Server integrates with Pydantic AI to ensure every task modification is structurally sound. When your agent calls `create_task` or `update_task`, the framework validates the inputs against Python models. If the LLM attempts to pass an invalid project ID or malformed text, Pydantic AI catches it before hitting the network. This prevents broken entries in your workspace and keeps your board data clean.

Type-safe team lookups via Pydantic AI

The Flow MCP Server exposes your roster to Pydantic AI using `list_workspace_members` and `list_workspace_teams`. The framework parses the returned JSON into typed Python objects at runtime. Your agent uses these typed models to safely map tasks. When calling `add_task_comment`, you can guarantee the target user ID matches your internal database schemas exactly.

Structural project parsing with Pydantic AI

Your Pydantic AI agent reads project hierarchies using `list_projects` and `list_task_lists`. Every returned list is validated against strict Pydantic models to prevent silent corruption. The agent can safely navigate from `get_project` down to individual tasks using `get_task`. If the API structure changes, your application fails loudly with a validation error instead of processing bad data.

Setup guide

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

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

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

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Common questions about Flow MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and use the `MCPToolset` constructor with your Vinkius HTTP URL. Pass the toolset instance directly into your `Agent` definition.

Yes, the framework validates the output of tools like `list_tasks` against its internal schemas. This guarantees your agent receives clean, structured data every time.

Yes, the integration uses the Streamable HTTP transport. The server runs externally on Vinkius, and your Pydantic AI agent connects using your secure endpoint token.

The framework is model-agnostic. You can run these MCP tools with OpenAI, Anthropic, Gemini, or even local models running on your own machine.

Your workspace IDs, task descriptions, and team rosters are processed inside an ephemeral V8 sandbox. No credentials are saved on disk, and all API calls are signed using a single-use token.

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