How to Use the Asana MCP in Pydantic AI

Q: What's the setup for connecting Asana to a Pydantic AI agent via its MCP Server?

You install the pydantic-ai-slimmcp package and then create an MCPToolset instance with your Vinkius URL. That toolset is passed to the Agent constructor's toolsets list. It's a clean setup that keeps your Asana tools organized.

Q: How do I list my Asana tasks in a type-safe way using Pydantic AI?

Your agent calls the listtasks tool with a project ID. Pydantic AI receives the JSON list of tasks from the server and automatically parses it into a Python list of Task objects, each one fully typed and validated.

Get type-safe Asana automation for any LLM. Pydantic AI validates every MCP response so your agent never works with bad data.

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Connect Asana MCP to Pydantic AI

Create your Vinkius account to connect Asana 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 Asana with Pydantic AI

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Claude

Perplexity

Reliably Create and Update Tasks

Your agent gets simple tools to `create_task` and `update_task` in Asana. It feels straightforward, but the real magic is in the framework's guarantees. When Asana responds, Pydantic AI forces the data into a strict model. If a field is missing, has an unexpected `null` value, or is the wrong data type, your code gets a `ValidationError` immediately. This prevents your agent from running with corrupted or incomplete task information.

Navigate Asana with Type-Safe Models

The server provides tools to explore your projects, like `list_projects`, `list_tasks`, and `list_sections`. Your agent receives clean, structured data for each. Because every list and item is a validated Pydantic object, you can skip writing defensive code. You just loop through the results. There's no need to check if a `task` object actually has a `name` key—Pydantic AI already guaranteed it for you.

Build Correct Agents with this MCP Server

Give your agent the `search_tasks` tool to find work items across your Asana workspace. The agent can construct a query, and the MCP server will return a list of matching tasks. This is where Pydantic AI's focus on correctness pays off. The search results aren't just a blob of JSON; they're a list of validated `Task` models. Your agent can then confidently pipe those results into other tools like `update_task`, knowing the data integrity is sound.

Setup guide

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

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

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

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Common questions about Asana MCP in Pydantic AI

Pydantic AI automatically validates every response from the Asana server against a Pydantic model. If the data doesn't match the expected structure—for example, a required field is missing—it raises a `ValidationError` in your code instead of letting your agent use bad data.

Yes. Pydantic AI is model-agnostic, so you can point it at any local or private LLM that has function-calling abilities. This server handles the connection to Asana, and Pydantic AI handles the local validation, regardless of which model you use.

You install the `pydantic-ai-slim[mcp]` package and then create an `MCPToolset` instance with your Vinkius URL. That toolset is passed to the `Agent` constructor's `toolsets` list. It's a clean setup that keeps your Asana tools organized.

Your agent calls the `list_tasks` tool with a project ID. Pydantic AI receives the JSON list of tasks from the server and automatically parses it into a Python list of `Task` objects, each one fully typed and validated.

It processes your Asana user data, project and task definitions, and the activity stories for tasks. For security, every tool call runs inside an ephemeral container on Vinkius. The container and any data within it are destroyed the moment the request is complete.

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