How to Use the GoatCounter MCP in Pydantic AI

Q: Can I use GoatCounter to log events in Pydantic AI?

Use counthits to send events. Your agent can validate the event payload before it ever reaches the server.

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Connect GoatCounter MCP to Pydantic AI

Create your Vinkius account to connect GoatCounter 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 GoatCounter with Pydantic AI

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Type-safe traffic data for Pydantic AI

Validate every response from `get_stats_total` against your Pydantic models. If the API structure changes, your agent stops before processing bad data. You maintain strict control over your data schema. It ensures your agent only acts on accurate, structured metrics from your analytics account.

Manage sites via Pydantic AI

Update site configurations with `update_site` while validating the payload. It prevents invalid settings from hitting your account. You gain peace of mind knowing the agent enforces your data types. Any deviation from the schema results in an immediate, clear error.

Export analytics in Pydantic AI

Run `start_export` and handle the resulting data objects within your type-safe pipeline. It keeps your reporting consistent and reliable. Your agent handles the export flow securely. Every status check through `get_export` is validated against expected return types.

Setup guide

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

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

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

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Common questions about GoatCounter MCP in Pydantic AI

The server provides clear schemas for every tool. Pydantic AI checks the API output against these models, ensuring your agent never processes malformed data.

Absolutely. Because every tool has a defined schema, your agent knows exactly what data to expect for every query.

Use `count_hits` to send events. Your agent can validate the event payload before it ever reaches the server.

Pass the server URL to the toolset constructor. The framework handles the rest, connecting your agent to the API.

It collects zero identifiable information. The server only records aggregate hits, ensuring your analytics remain compliant without handling sensitive user records.

Use it with your favorite AI tools

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