How to Use the Anaplan MCP in Pydantic AI

Q: How does Pydantic AI validate Anaplan tool arguments from this MCP Server?

The MCP Server provides strict JSON schemas for tools like runimport. Pydantic AI intercepts the LLM output and validates it against these schemas before hitting the Anaplan API.

Q: Can I run asynchronous Anaplan processes in Pydantic AI?

Yes, you can run runprocess asynchronously. Your Pydantic AI agent then polls gettaskstatus in the background to handle long-running calculations without blocking.

Q: What happens if Anaplan returns an unexpected file structure?

When querying listfiles, Pydantic AI validates the file metadata. If the server returns data that violates the schema, the framework raises a validation error instead of passing bad data to your agent.

Validate Anaplan data actions with type-safe execution in Pydantic AI using this MCP Server.

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Connect Anaplan MCP to Pydantic AI

Create your Vinkius account to connect Anaplan 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 Anaplan with Pydantic AI

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Validate Anaplan model queries with Pydantic AI

The `list_models` tool fetches all available models within a workspace and feeds them directly into your Pydantic AI agent. Runtime validation checks the returned model IDs against strict type schemas before your code ever runs. If you need to find specific workspaces first, the agent uses `list_workspaces` to get a structured list. Because Pydantic AI enforces runtime types, your agent cannot pass malformed IDs to downstream tools.

Run type-safe imports and exports

The `run_import` tool triggers data ingestion while Pydantic AI guarantees the parameters match Anaplan requirements. This prevents runtime API errors caused by your agent hallucinating invalid file names or import IDs. For outbound data, the agent runs `run_export` and verifies the export task configuration. The agent then queries `list_exports` to confirm the target export action is ready for execution.

Track execution states using this MCP Server

The `get_task_status` tool provides real-time progress updates for running processes back to Pydantic AI. Parsing the status payload ensures the job succeeded before the agent proceeds to downstream logic. Your agent starts these jobs using `run_process` to execute multi-step actions. If the process fails, the agent catches the validation error immediately, preventing corrupt data states.

Setup guide

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

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

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

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Common questions about Anaplan MCP in Pydantic AI

The MCP Server provides strict JSON schemas for tools like `run_import`. Pydantic AI intercepts the LLM output and validates it against these schemas before hitting the Anaplan API.

Yes, you can run `run_process` asynchronously. Your Pydantic AI agent then polls `get_task_status` in the background to handle long-running calculations without blocking.

The server handles your financial planning data and import definitions through encrypted transport channels. No data is stored permanently on the Vinkius platform, ensuring your corporate forecasts remain private.

When querying `list_files`, Pydantic AI validates the file metadata. If the server returns data that violates the schema, the framework raises a validation error instead of passing bad data to your agent.

No, Vinkius hosts the server externally. You connect your Pydantic AI agent using the Streamable HTTP transport pointing to your managed endpoint.

Use it with your favorite AI tools

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