How to Use the Drone CI MCP in Pydantic AI

Q: How do I configure Pydantic AI to use the Drone CI toolset?

Install the library using pip install "pydantic-ai-slimmcp". Initialize the MCPToolset with your Vinkius HTTP endpoint and pass it to your Agent constructor. The agent will automatically register all 39 tools.

Q: Can my agent manage user accounts on Drone CI?

Yes, if the agent has admin privileges, it can use createuser, updateuser, and deleteuser. The Pydantic AI schemas ensure that user creation payloads are validated before the API request is sent.

Q: How does the Pydantic AI integration handle build promotions?

The agent triggers promotions using promotebuild to push artifacts to target environments. You can write custom Pydantic validators to ensure the agent only promotes builds that have successfully passed all previous stages.

Control Drone CI pipelines with type-safe Python agents using Pydantic AI to guarantee runtime data integrity.

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Connect Drone CI MCP to Pydantic AI

Create your Vinkius account to connect Drone CI 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 Drone CI with Pydantic AI

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Type-safe pipeline monitoring and logging

`get_build` returns structured pipeline data that Pydantic AI validates against strict schemas at runtime. If the Drone API structure changes, the agent catches the mismatch immediately instead of acting on corrupted data. This prevents silent execution failures in production. When debugging, the agent fetches logs using `get_build_logs` to analyze step failures. The framework ensures that the log payloads conform to expected string formats before passing them to the model. This guarantees clean input for your agent's reasoning loop.

Secure secret management via Pydantic AI

`create_secret` registers new environment variables for your builds with strict type validation on the payload. The agent uses `update_secret` and `delete_secret` to manage these values without risking malformed API requests. This keeps your deployment credentials secure. By using this MCP Server, your agent can audit active secrets using `list_secrets`. The returned list is parsed into typed Python models, allowing the agent to flag weak or outdated keys. You get an automated, type-safe security auditor for your CI/CD pipelines.

Programmatic repository configuration and repair

`update_repo` modifies repository settings like timeout limits and build visibility. If a webhook breaks, the agent calls `repair_repo` to restore communication between your git provider and Drone. This keeps your automation pipeline running without human intervention. The agent uses `get_repo` to inspect the current state before making any modifications. Because Pydantic AI enforces type boundaries, you can be sure the configuration payload matches Drone's API requirements exactly. This eliminates configuration errors.

Setup guide

Set up Drone CI 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": {
        "drone-ci-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Drone CI MCP in Pydantic AI

Install the library using `pip install "pydantic-ai-slim[mcp]"`. Initialize the `MCPToolset` with your Vinkius HTTP endpoint and pass it to your Agent constructor. The agent will automatically register all 39 tools.

Using this MCP Server enforces strict runtime validation on every tool response, such as `list_builds` or `get_build`. If the API returns unexpected fields, the framework raises a validation error, preventing the agent from making decisions based on bad data.

The integration uses an external MCP Server running on Vinkius, which handles the direct API connection to Drone. Secrets managed via `create_secret` are transmitted over encrypted TLS channels and never stored in plain text or exposed to the model's training data.

Yes, if the agent has admin privileges, it can use `create_user`, `update_user`, and `delete_user`. The Pydantic AI schemas ensure that user creation payloads are validated before the API request is sent.

The agent triggers promotions using `promote_build` to push artifacts to target environments. You can write custom Pydantic validators to ensure the agent only promotes builds that have successfully passed all previous stages.

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