How to Use the Apiary MCP in Pydantic AI

Build type-safe API workflows using Pydantic AI to validate every Apiary response at runtime.

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Connect Apiary MCP to Pydantic AI

Create your Vinkius account to connect Apiary 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 Apiary with Pydantic AI

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Type-safe API blueprint parsing with Pydantic AI

When your agent fetches API designs, a single malformed field can break your entire pipeline. This MCP Server integrates with Pydantic AI to validate every response from tools like `get_api` and `list_apis` against strict runtime schemas. If Apiary returns unexpected data structures, the framework raises a validation error immediately. This setup eliminates silent failures. You declare an MCP toolset pointing to your Vinkius URL and pass it to your agent, ensuring your Python code only operates on guaranteed, validated data.

Validate team rosters and permissions

Managing workspace access requires absolute precision. Your agent can call `list_team_members` and `list_teams` to audit user permissions across your Apiary organization. Pydantic AI parses the returned member lists and team objects, converting them into typed Python models. You can write custom validation rules to flag external email addresses or unauthorized users. The agent can then automatically notify your security team or update access lists.

Verify test suite results with strict validation

Running API tests shouldn't involve parsing raw, unpredictable text blocks. The agent invokes `run_tests` to execute your Dredd suites and receives structured test metrics. Pydantic AI validates the test outcomes, ensuring your agent acts on precise pass/fail counts rather than guessing. This MCP integration lets the agent automatically parse the error details and attempt to fix the blueprint file. Once corrected, it uses `publish_blueprint` to upload the working design.

Setup guide

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

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

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

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Common questions about Apiary MCP in Pydantic AI

Install the slim package with MCP support and initialize a toolset pointing to your Vinkius URL. Pass this toolset directly into your `Agent` constructor to expose the tools.

Yes, the framework supports both Streamable HTTP and SSE transports. You can configure the `MCPToolset` to use either transport depending on your application's architecture.

The framework will raise a validation error instantly instead of letting the MCP client process corrupted data. This prevents your agent from making decisions based on incomplete or hallucinated API designs.

Yes, Pydantic AI is model-agnostic and works with any supported backend. You can use the validated Apiary tools with OpenAI, Anthropic, Gemini, or any local LLM running on your machine.

Your API blueprints, test reports, and team lists are processed inside a zero-trust, ephemeral V8 sandbox. Vinkius handles the authorization token securely, ensuring your sensitive API designs never leak into public model training sets.

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