How to Use the Glama MCP in Pydantic AI

Q: How do I use Pydantic AI with the Glama MCP server?

Instantiate an MCPToolset with the Glama URL and pass it to your agent. This automatically registers the available tools and enforces type safety on every response.

Q: Can I list available models in Glama for Pydantic AI agents?

Yes. Invoke glamagetgatewaymodels to get a structured list. Your agent can then validate these model names against your Pydantic schemas before making a request.

Q: How does Pydantic AI handle private instances via Glama?

Call glamagethostedinstances to identify your private servers. Your agent handles these connections as typed resources, ensuring consistent access across your infrastructure.

Q: Is telemetry supported in Pydantic AI with Glama?

Yes. Use glamasendtelemetry to report your agent's tool usage. The metrics are sent to the gateway, providing a validated log of your agent's actions.

Add strict type validation to your Pydantic AI agents using the Glama MCP server directory.

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Connect Glama MCP to Pydantic AI

Create your Vinkius account to connect Glama 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 Glama with Pydantic AI

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Type-safe tool discovery with Glama

Load the Glama toolset into your `Agent` definition to enable runtime schema validation. If the directory returns unexpected data, your agent throws a clear error. Use `glama_list_mcp_servers` to search for new capabilities. Each response is validated against your Pydantic models to ensure the tool definition matches your requirements.

Validate gateway chat responses

Route agent calls through `glama_run_gateway_chat` and map the output to your schema. This prevents hallucinated fields from corrupting your agent state. Fetch model configurations with `glama_get_gateway_model_details` to verify the expected response structure. Your agent only proceeds if the model parameters match your type definitions.

Manage MCP server info in Pydantic AI

Extract server metadata with `glama_get_mcp_server_info` to populate your configuration models. It ensures your agent always has the correct connection context. Use `glama_get_mcp_attributes` to filter out irrelevant servers. By locking your toolset to specific attributes, you maintain absolute control over what your agent can execute.

Setup guide

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

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

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

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Common questions about Glama MCP in Pydantic AI

Instantiate an `MCPToolset` with the Glama URL and pass it to your agent. This automatically registers the available tools and enforces type safety on every response.

Yes. Invoke `glama_get_gateway_models` to get a structured list. Your agent can then validate these model names against your Pydantic schemas before making a request.

Call `glama_get_hosted_instances` to identify your private servers. Your agent handles these connections as typed resources, ensuring consistent access across your infrastructure.

Yes. Use `glama_send_telemetry` to report your agent's tool usage. The metrics are sent to the gateway, providing a validated log of your agent's actions.

The server validates every incoming response against your defined Pydantic types. This process ensures only structured, verified data touches your application logic, keeping your sensitive fields safe from unexpected API modifications.

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