How to Use the Gerrit MCP in Pydantic AI

Q: How do I integrate the Gerrit MCP server into Pydantic AI?

Install the package with pydantic-ai-slimmcp and initialize an MCPToolset with your server URL. Pass it to your agent as a toolset.

Q: Can I list my Gerrit email addresses in Pydantic AI?

Yes, call listemails. The returned data is immediately validated against your model to ensure account consistency.

Use Pydantic AI with the Gerrit MCP Server for type-safe code review automation that prevents silent data corruption.

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Connect Gerrit MCP to Pydantic AI

Create your Vinkius account to connect Gerrit 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 Gerrit with Pydantic AI

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Validate Gerrit data with Pydantic AI models

Every response from `get_change` is parsed against a Pydantic model. If the API returns a malformed field, the agent catches it instantly instead of hallucinating values. This provides a strict contract for your agent. It ensures that data from `query_changes` is always structured correctly before the agent makes a decision on a review.

Manage Gerrit project metadata safely

Your agent calls `list_projects` to gather repository info. Because you use Pydantic AI, the resulting list is validated against your schema at runtime. This keeps your agent logic predictable. It uses `list_branches` to verify repository paths, ensuring it never attempts to operate on a non-existent branch.

Track Gerrit reviewers and patch sets

The agent tracks who reviewed a change by calling `list_reviewers`. The Pydantic model enforces that the reviewer IDs and names are always in the expected format. When auditing progress, it uses `list_patchsets` to inspect revision history. It rejects any data that doesn't match your defined types, keeping your agent logic bulletproof.

Setup guide

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

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

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

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Common questions about Gerrit MCP in Pydantic AI

Install the package with `pydantic-ai-slim[mcp]` and initialize an `MCPToolset` with your server URL. Pass it to your agent as a toolset.

Pydantic AI will throw a validation error. This prevents your agent from acting on corrupt or missing information from the API.

Yes, call `list_emails`. The returned data is immediately validated against your model to ensure account consistency.

Yes. The toolset supports Streamable HTTP and SSE transports, ensuring smooth communication between your agent and the server.

The server only exposes the specific tools you call. All communication is routed through a secure, ephemeral sandbox that isolates your account information from other processes.

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