How to Use the COR MCP in Pydantic AI

Q: Is the COR setup in Pydantic AI stable?

Yes, by using the unified MCPToolset approach, you get a clean connection. It handles both SSE and Streamable HTTP transports without extra configuration.

Q: How do I fetch COR project details in Pydantic AI?

Use getcorproject within your agent toolset. The result is parsed and validated against your model, ensuring no hallucinated fields appear.

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Connect COR MCP to Pydantic AI

Create your Vinkius account to connect COR 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 COR with Pydantic AI

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Pydantic AI agents verify COR responses

Every call like `get_cor_task` runs through your schema definitions. If the server returns something unexpected, the agent catches the validation error immediately. You avoid silent failures that plague other setups. Your agent only acts when the data matches your strict requirements.

Managing team resources in Pydantic AI

Call `list_cor_team_users` to see who is on the roster. The response is validated against your Pydantic models to ensure the data structure is perfect. Your agent can trust the output of `list_cor_teams` implicitly. It builds a reliable map of your organization for every task assignment.

Pydantic AI and COR status checks

Use `check_cor_status` to ensure your connection is live before running heavy tasks. Your agent checks the health of the integration first. It handles the response gracefully. If the status isn't nominal, the agent stops before wasting tokens on failed attempts.

Setup guide

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

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

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

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Common questions about COR MCP in Pydantic AI

The framework matches the JSON response from the server against your predefined models. Any field mismatch triggers a clear validation error before the agent tries to use the data.

You can. The server works with any model-agnostic agent, so you can swap your backend without changing how you call these tools.

Yes, by using the unified `MCPToolset` approach, you get a clean connection. It handles both SSE and Streamable HTTP transports without extra configuration.

Use `get_cor_project` within your agent toolset. The result is parsed and validated against your model, ensuring no hallucinated fields appear.

Your time entries and sensitive agency information are never stored on our side. We only provide the tunnel for your agent to interact with the COR API securely.

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