How to Use the Hygraph (Headless CMS) MCP in Pydantic AI

Q: Can Pydantic AI handle localized content schemas from Hygraph (Headless CMS)?

Yes, by utilizing listprojectlocales to retrieve active translation spaces. Your Pydantic AI agent can map these locales to validated type schemas, ensuring localized mutations sent via executegraphqlmutation conform to your localized field types in Hygraph (Headless CMS).

Build type-safe Pydantic AI workflows that validate Hygraph (Headless CMS) schema mutations and document updates at runtime.

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Connect Hygraph (Headless CMS) MCP to Pydantic AI

Create your Vinkius account to connect Hygraph (Headless CMS) 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 Hygraph (Headless CMS) with Pydantic AI

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Enforce strict runtime validation with Pydantic AI

Calling `execute_graphql_query` and `get_model_fields` lets your type-safe agent validate every schema query and document write at runtime. By combining this MCP Server with your type-safe agent, every schema query and document write is validated against strict Pydantic models at runtime. This strict validation ensures that your application state remains completely predictable. You can confidently map CMS fields fetched via those tools to your internal Python types, catching structural errors before they hit your frontend.

Safely execute mutations with zero structural errors

Using `create_cms_document` and `update_cms_document` ensures your agent can modify content while matching your Python schemas exactly. If the agent attempts to write a string to an integer field, the validation layer catches it instantly. Once the data passes validation, the agent can safely run `publish_cms_document` to release the changes. This setup gives you a bulletproof content pipeline where your agent can never corrupt your production database.

Automate schema introspection and model generation

Invoking `list_schema_introspection` allows your agent to fetch the complete model structure of your project dynamically. You can then use this data to generate or update your Pydantic schemas on the fly. This dynamic setup allows your agent to adapt to schema changes without requiring manual code updates. The agent simply inspects the schema, updates its internal models, and continues executing queries like `execute_graphql_query` with perfect type safety.

Setup guide

Set up Hygraph (Headless CMS) 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": {
        "hygraph-headless-cms-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Hygraph (Headless CMS) transactions")
print(result.output)

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Common questions about Hygraph (Headless CMS) MCP in Pydantic AI

Install the pydantic-ai-slim package with the mcp extra, then initialize MCPToolset with your Vinkius HTTP endpoint. Pass this toolset directly into your Agent's toolsets list to register tools like `execute_graphql_query`.

The system will raise a validation error and fail loudly. This prevents your application from processing malformed data returned by tools like `execute_graphql_query`, allowing your Pydantic AI agent to catch the error and adjust.

Yes, by utilizing `list_project_locales` to retrieve active translation spaces. Your Pydantic AI agent can map these locales to validated type schemas, ensuring localized mutations sent via `execute_graphql_mutation` conform to your localized field types in Hygraph (Headless CMS).

No, you should use the unified MCPToolset class pointing to your server's HTTP or SSE endpoint. This class replaces deprecated connection methods, providing a cleaner way to expose MCP tools like `update_cms_document` to your agent.

Your sensitive API tokens are never exposed to the agent or stored in your Python runtime. Vinkius handles the underlying authentication inside an isolated, zero-trust sandbox, keeping your Pydantic AI agent interactions with CMS document payloads and schema writes completely secure.

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