How to Use the Octoboard MCP in Pydantic AI

Q: How do I use Pydantic AI with the Octoboard MCP Server?

Initialize the MCPToolset with your server URL and pass it to your agent. Pydantic AI will automatically handle the schema validation for all tools.

Build type-safe marketing reports with Pydantic AI and Octoboard. Validate every metric before it hits your dashboard.

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Connect Octoboard MCP to Pydantic AI

Create your Vinkius account to connect Octoboard to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Octoboard with Pydantic AI

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Type-safe data pushes with Pydantic AI

Every Octoboard tool response is validated against Pydantic models. If `push_numeric_metric` or `push_bar_chart` receives malformed data, your agent catches it immediately. This prevents silent corruption in your client reports. You get strict runtime guarantees that your dashboard data matches your agent's output.

Automate client report delivery

Use `list_automated_reports` to identify what needs updating. Your agent selects the correct report and pushes fresh data using `push_data_table`. It handles the heavy lifting of report maintenance. You just define the logic, and the agent keeps the tables updated for your clients.

Audit your API health

Your agent can check API usage with `check_api_usage` before running a heavy batch of updates. It ensures your reporting pipeline stays within limits. Reliability is built into the workflow. If the API status is degraded, your agent knows not to attempt a push, preventing partial updates.

Setup guide

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

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

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

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Common questions about Octoboard MCP in Pydantic AI

Initialize the `MCPToolset` with your server URL and pass it to your agent. Pydantic AI will automatically handle the schema validation for all tools.

The agent will raise a validation error. You won't have to worry about bad data reaching your dashboard, as the framework blocks it at runtime.

Yes, the toolset is model-agnostic. Whether you use a local model or a cloud-based one, the Pydantic validation ensures the Octoboard interaction remains consistent.

Yes, it supports both Streamable HTTP and SSE. You can configure your Pydantic AI setup to use the transport that best fits your infrastructure.

Your endpoint token authenticates every request. Since the agent validates everything against Pydantic models, only authorized and structured data ever leaves your environment.

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