How to Use the Codacy MCP in Pydantic AI

Use the Codacy MCP Server with Pydantic AI for type-safe code quality automation.

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Connect Codacy MCP to Pydantic AI

Create your Vinkius account to connect Codacy 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 Codacy with Pydantic AI

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Validate Codacy profiles in Pydantic AI

Call `get_my_codacy_profile` to pull user data into your agent. Pydantic AI validates the response against your defined models. If the API returns a malformed structure, the agent stops immediately. You avoid the silent failures common in other frameworks.

List organizations with Pydantic AI

Execute `list_codacy_organizations` to map your workspace. Pydantic AI ensures the output matches your expected schema. This forces your agent to handle data strictly. You get reliable state tracking without worrying about unexpected null fields.

Search quality issues in Pydantic AI

Run `search_repository_issues` to find specific bugs. Pydantic AI checks every result against your type definitions before the agent takes action. It guarantees that the data your agent processes is clean. You never have to deal with hallucinated issue fields.

Setup guide

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

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

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

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Common questions about Codacy MCP in Pydantic AI

Install the pydantic-ai-slim package. Define the MCPToolset and pass it to your Agent object for immediate, typed access.

Yes, it validates every response from the Codacy server. If the data doesn't match your Pydantic models, the agent raises an error.

The toolset is model-agnostic. It works with whatever LLM you have configured in your Pydantic AI agent.

It supports both Streamable HTTP and SSE transports. Use the approach that fits your external server deployment.

The server handles only non-sensitive quality metrics. Since Pydantic AI validates every byte, you have full control over what data enters your agent.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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