How to Use the DeveloperHub MCP in Pydantic AI

Q: Is setting up the DeveloperHub MCP server with Pydantic AI complicated?

No, it's one line. You create an MCPToolset with your Vinkius server URL and pass it to your Agent. Pydantic AI handles the tool discovery and validation automatically.

Q: Does this DeveloperHub server work with the unified MCPToolset in Pydantic AI?

Yes, it's designed for the modern MCPToolset. You don't need to mess with the deprecated MCPServerHTTP class. Just pass the URL to the toolset and you're set.

Bring type-safe validation to your DeveloperHub docs. Pydantic AI ensures your agent never works with corrupt data.

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Connect DeveloperHub MCP to Pydantic AI

Create your Vinkius account to connect DeveloperHub 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 DeveloperHub with Pydantic AI

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Your agent uses `get_documentation_page_content` to fetch a page, and Pydantic AI automatically validates the response against a strict schema. If the DeveloperHub API ever returns an unexpected field or a wrong data type, your agent stops cold with a `ValidationError`. This means no silent failures. You find out immediately if an API contract changes, instead of discovering downstream that your agent has been processing garbage data for days.

Swap Models, Keep the Tools

Build your documentation agent once. This MCP server works with Pydantic AI to let you switch between OpenAI, Gemini, or a local model without changing a single line of your tool-using code. Your calls to `list_documentation_projects` or `search_documentation_portal` stay the same. The tool definitions are completely separate from the LLM logic. This gives you real flexibility. You can test with a fast, cheap model and deploy with a powerful, expensive one, all while your DeveloperHub integration remains constant and type-safe.

Structure Content with Pydantic AI

Use `list_product_changelogs` to get a raw list of updates. Then, use a Pydantic AI agent to parse that text into structured Pydantic objects. You define a `ChangelogEntry` model, and the agent handles the messy work of extraction. This turns unstructured text into clean, validated Python objects you can actually build reliable automation on. You're no longer just pattern-matching strings; you're working with a real data model.

Setup guide

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

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

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

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Common questions about DeveloperHub MCP in Pydantic AI

When this MCP server returns data, Pydantic AI automatically tries to parse it into a Pydantic model. If the data doesn't perfectly match the model's schema—a string where an int should be, a missing field—it raises a `ValidationError` on the spot. No bad data gets through.

No, it's one line. You create an `MCPToolset` with your Vinkius server URL and pass it to your `Agent`. Pydantic AI handles the tool discovery and validation automatically.

Yes. Pydantic AI is model-agnostic. You can point it to a local model running in Ollama or another service, and it will use the same DeveloperHub tools from this server without any changes to your tool code.

Yes, it's designed for the modern `MCPToolset`. You don't need to mess with the deprecated `MCPServerHTTP` class. Just pass the URL to the toolset and you're set.

The agent can access data provided by the DeveloperHub tools: your documentation content, project structures, changelogs, and account metadata. Pydantic AI itself doesn't store this data; it just validates its structure in memory at runtime. The connection is ephemeral and secured by your Vinkius token.

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