Can the agent fetch the exact mock server endpoints for one of my APIs?

Yes. You can use the `get_doc_url` capability to retrieve both the public facing documentation url and the private mock server endpoint string. Your agent can immediately use this url to simulate requests internally using other modules or tests.

Am I able to update the Blueprint markup completely through the agent?

Absolutely. Instruct your AI agent to fetch the blueprint with `get_api`. Once read into context, prompt the agent to make your desired changes (e.g. 'Add a new POST route for user creation'). Then, instruct it to use `publish_blueprint` to upload the newly revised markdown.

How does the team feature list function mapping work?

Apiary enables collaboration. Utilizing `list_teams`, the agent finds the slugs of your organizations. Then, using `list_team_apis`, it fetches all collaborative APIs available for that precise team. This hierarchy avoids hallucinating random endpoint repositories.

Apiary MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Apiary
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Apiary through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Apiary "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Apiary?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Apiary MCP Server

Connect your Apiary workspace to your AI agent and take full control of your API design, specification, and validation processes through natural conversation.

Pydantic AI validates every Apiary tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Read API Blueprints — Fetch raw Markdown-based API Blueprints or Swagger files defining endpoints, methods, and parameters without leaving your editor
Publish Documentation — Instantly update your Apiary projects with new markdown code. Mock servers and public documentation sync automatically
Run Dredd Tests — Validate your backend implementation against the blueprint specifications using integrated tests
Team Management — Query team API projects, list team members, and manage your engineering ecosystem intuitively

The Apiary MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Apiary to Pydantic AI via MCP

Follow these steps to integrate the Apiary MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Apiary with type-safe schemas

Why Use Pydantic AI with the Apiary MCP Server

Pydantic AI provides unique advantages when paired with Apiary through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Apiary integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Apiary connection logic from agent behavior for testable, maintainable code

Apiary + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Apiary MCP Server delivers measurable value.

Type-safe data pipelines: query Apiary with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Apiary tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Apiary and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Apiary responses and write comprehensive agent tests

Apiary MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Apiary to Pydantic AI via MCP:

get_api

Get the full API Blueprint source code of an Apiary API project

get_doc_url

Get the documentation and mock server URLs for an Apiary API project

get_me

Get the authenticated Apiary user profile

get_team

Get details of a specific Apiary team

list_apis

Apiary is an API design-first platform. List all API projects on Apiary

list_team_apis

List all API projects belonging to a specific Apiary team

list_team_members

List all members of an Apiary team

list_teams

List all teams the authenticated user belongs to on Apiary

publish_blueprint

Use with valid Markdown blueprint/Swagger. Publish (update) the API Blueprint of an Apiary API project

run_tests

Get or run Dredd-style API tests against an Apiary project

Example Prompts for Apiary in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Apiary immediately.

"Get the API Blueprint code for my project named 'payments-gateway'."

"List all team members of the 'frontend-team' in Apiary."

"Update my 'users-api' with this new blueprint code..."

Troubleshooting Apiary MCP Server with Pydantic AI

Common issues when connecting Apiary to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Apiary + Pydantic AI FAQ

Common questions about integrating Apiary MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Apiary MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.