How to Use the MPU-Manager MCP in Pydantic AI

Q: How do I register the MPU-Manager MCP Server in Pydantic AI?

Use the unified MCPToolset with your Vinkius HTTP endpoint. Pass this toolset directly into the toolsets list when initializing your Pydantic AI agent.

Q: What happens if the MPU-Manager API is down?

Your agent can run checkmpumanagerstatus to verify connectivity. If the connection fails, Pydantic AI will catch the network error, allowing you to handle the fallback gracefully.

Q: How do I inspect a specific incident report?

Have your agent call getcase to grab the case details, then run listreports to pull the associated documentation. Every field is validated at runtime to prevent silent failures.

Run type-safe broadcast scheduling and crew management with Pydantic AI and this MCP Server.

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Connect MPU-Manager MCP to Pydantic AI

Create your Vinkius account to connect MPU-Manager 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 MPU-Manager with Pydantic AI

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Type-Safe Scheduling with Pydantic AI and MCP

`create_appointment` schedules crew shifts while validating every input field against strict Python types. If your agent tries to write an invalid date or missing crew ID, Pydantic AI rejects it before hitting your database. To check existing schedules, the agent calls `list_appointments`. The response is parsed into clean Python models, ensuring your application never crashes on unexpected API payloads.

Catch Data Anomalies in Production Cases

`create_case` logs production incidents with strict runtime schema validation. This ensures that critical fields like priority levels and technical notes conform exactly to your system's specs. When pulling reports, the agent uses `list_reports` to fetch historical documentation. Because this MCP Server runs on a structured schema, your agent receives clean, predictable data every time.

Structured Client Queries for Live Operations

`get_client` retrieves client details with guaranteed type safety. If the API returns a string where an integer was expected, Pydantic AI raises a validation error immediately. The agent can also query `list_clients` to pull entire rosters. This structured validation keeps your live operational dashboard accurate, preventing corrupted data from breaking your UI.

Setup guide

Set up MPU-Manager 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": {
        "mpu-manager-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent MPU-Manager transactions")
print(result.output)

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Common questions about MPU-Manager MCP in Pydantic AI

Use the unified `MCPToolset` with your Vinkius HTTP endpoint. Pass this toolset directly into the `toolsets` list when initializing your Pydantic AI agent.

Your agent can run `check_mpumanager_status` to verify connectivity. If the connection fails, Pydantic AI will catch the network error, allowing you to handle the fallback gracefully.

Yes, Pydantic AI supports both Streamable HTTP and SSE transports. This lets you maintain long-lived connections for real-time schedule tracking.

Have your agent call `get_case` to grab the case details, then run `list_reports` to pull the associated documentation. Every field is validated at runtime to prevent silent failures.

Your crew schedules and client records are protected by Vinkius's zero-trust sandbox. The validation happens locally in your Python runtime, and your API keys are never exposed to the public internet.

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