How to Use the Alpic MCP in Pydantic AI

Q: How do I register Alpic MCP Server with my Pydantic AI agent?

Create an MCPToolset pointing to your Alpic server HTTP URL. Pass this toolset directly into your Pydantic AI Agent constructor within the toolsets list to enable type-safe infrastructure management.

Q: Does Pydantic AI validate Alpic environment creation?

Yes, when your Pydantic AI agent calls createenvironment, the input parameters are validated against Pydantic schemas. This ensures your Alpic project IDs and environment names are correctly formatted before hitting the API.

Q: How does Alpic handle local debugging with Pydantic AI MCP tools?

Your Pydantic AI agent calls gettunnelticket to obtain a secure local development tunnel. This lets you run local validation tests against your custom Alpic schemas before deploying.

Q: Can Pydantic AI manage multiple Alpic projects?

Yes, your Pydantic AI agent executes listprojects to retrieve all active environments. The resulting list is parsed into typed models, allowing your agent to safely select which Alpic server to update.

Control your Alpic MCP Server infrastructure with strict type-safety using Pydantic AI validation schemas.

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Connect Alpic MCP to Pydantic AI

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Setup Alpic with Pydantic AI

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Type-safe Alpic MCP Server deployments in Pydantic AI

`deploy_environment` starts asynchronous Alpic builds while Pydantic AI validates the payload structure at runtime. Your agent tracks the progress using `get_deployment`, ensuring all returned metadata matches your strict Python models. If the Alpic build output changes, the Pydantic AI framework raises a validation error immediately. That blocks corrupted deployment states. Your production pipelines stay safe.

Validate Alpic configuration with Pydantic AI

`add_variable` updates your Alpic environment variables using validated Pydantic schemas. Your Pydantic AI agent guarantees that keys and values conform to your exact configuration types before writing them. The agent calls `list_variables` to audit active Alpic settings, parsing the masked outputs into typed Python objects. This keeps your Pydantic AI MCP configuration records clean and predictable.

Monitor Alpic health with strict validation

`get_project_analytics` returns structured Alpic usage metrics that your Pydantic AI agent parses into typed dataframes. The model evaluates Alpic server health against strict performance thresholds. If latency exceeds your limits, the Pydantic AI agent queries `get_deployment_logs` to diagnose the issue. Every log line is handled as a typed string to avoid parsing errors.

Setup guide

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

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

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

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Common questions about Alpic MCP in Pydantic AI

Create an `MCPToolset` pointing to your Alpic server HTTP URL. Pass this toolset directly into your Pydantic AI `Agent` constructor within the `toolsets` list to enable type-safe infrastructure management.

Yes, when your Pydantic AI agent calls `create_environment`, the input parameters are validated against Pydantic schemas. This ensures your Alpic project IDs and environment names are correctly formatted before hitting the API.

Your Pydantic AI agent calls `get_tunnel_ticket` to obtain a secure local development tunnel. This lets you run local validation tests against your custom Alpic schemas before deploying.

Yes, your Pydantic AI agent executes `list_projects` to retrieve all active environments. The resulting list is parsed into typed models, allowing your agent to safely select which Alpic server to update.

Alpic encrypts your tunnel tickets and project metadata using AES-256 encryption. When your Pydantic AI agent calls `get_tunnel_ticket`, the token is transmitted over an SSL connection and immediately invalidated after use.

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