How to Use the Tally MCP in Pydantic AI

Q: How do I ensure Tally data types are correct using Pydantic AI?

You must use getform first. This pulls the schema which your agent validates against a Pydantic model, guaranteeing that every piece of metadata returned is type-safe.

Q: What is the safest way to audit submissions with Pydantic AI?

The most reliable method is using getsubmission. It pulls specific records into a defined structure. This allows your agent to validate every field's content against its expected type.

Q: Can I list all available Tally forms using Pydantic AI?

Yes, run listforms. The results are typed and validated by the framework, so you get a clean, predictable list of form IDs to work with.

Q: Does Pydantic AI help manage data when deleting Tally submissions?

It does. When calling deletesubmission, your agent must pass correctly typed parameters; otherwise, the process fails immediately and loudly, preventing accidental data loss.

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Setup Tally with Pydantic AI

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Validate Form Structures

The first thing an agent needs is a guaranteed schema. Use `get_form` to pull the exact details of any Tally form. Because your framework validates responses, you get a reliable Pydantic model describing every field and data type. This prevents silent corruption. The agent knows exactly what fields it's dealing with before writing or passing the data to another service.

Manage Sensitive Records

Need to delete a submission? Use `delete_submission`. Your Pydantic AI layer acts as an extra set of guardrails, checking the parameters and confirming the action's intent before the call executes. This makes high-risk actions safe. The agent will fail loudly if the request structure is wrong, which is exactly what you want.

Audit System Scope

To know your boundaries, use `list_workspaces`. This lists all available workspaces, providing a clearly defined set of possible contexts for the agent. The agent can then cross-reference this with `list_forms` to map out every form that exists in every workspace, ensuring nothing is missed.

Setup guide

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

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

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

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Common questions about Tally MCP in Pydantic AI

You must use `get_form` first. This pulls the schema which your agent validates against a Pydantic model, guaranteeing that every piece of metadata returned is type-safe.

The most reliable method is using `get_submission`. It pulls specific records into a defined structure. This allows your agent to validate every field's content against its expected type.

Yes, run `list_forms`. The results are typed and validated by the framework, so you get a clean, predictable list of form IDs to work with.

It does. When calling `delete_submission`, your agent must pass correctly typed parameters; otherwise, the process fails immediately and loudly, preventing accidental data loss.

The server exposes metadata for each workspace, including IDs. Your agent treats these fields as validated strings or integers, guaranteeing predictable input/output types.

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