How to Use the BugBug MCP in Pydantic AI

Q: Does Pydantic AI support BugBug suite runs?

It does. You can trigger runsuite and rely on the typed return value to confirm the run has started successfully.

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Connect BugBug MCP to Pydantic AI

Create your Vinkius account to connect BugBug 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 BugBug with Pydantic AI

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Type-safe BugBug test execution in Pydantic AI

Trigger tests using `run_suite` and `run_test` while relying on Pydantic models to validate every response. If the MCP server returns malformed data, your agent crashes before bad data propagates. Use `get_test_run` to fetch status updates that are strictly typed. This prevents silent failures in your testing pipeline.

Catalog your BugBug tests via Pydantic AI

List your entire test library using `list_tests` and `list_suites`. Each entry is mapped to a model, ensuring your agent always knows the exact structure of your test suite. Call `list_projects` to get a structured view of your environment. It forces the agent to handle data that matches your expected schema.

Audit BugBug performance with Pydantic AI

Audit your history using `list_test_runs` and `list_suite_runs` to track regressions. The typed output ensures you can programmatically filter for failure states with zero ambiguity. Check your network configuration with `get_ips`. Your Pydantic AI agent verifies the response format, keeping your infrastructure logic error-free.

Setup guide

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

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

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

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Common questions about BugBug MCP in Pydantic AI

Every tool response is checked against runtime schemas. If BugBug returns data that doesn't match the expected type, the agent raises a validation error immediately.

Yes. By using the MCPToolset class, you ensure all communication is strictly typed, preventing the agent from hallucinating fields in the test data.

It's straightforward. Just define your MCPToolset with the server URL and pass it to your Agent instance to start running tests.

It does. You can trigger `run_suite` and rely on the typed return value to confirm the run has started successfully.

We use isolated memory for all validation tasks. Your test run logs and project identifiers are stripped of sensitive info before being handled by the agent.

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