How to Use the MSAAQ MCP in Pydantic AI

Q: How do I integrate MSAAQ with Pydantic AI?

Install the slim package and use MCPToolset to connect. Add the toolset to your agent, and Pydantic AI will handle the schema validation automatically.

Q: Can I use MSAAQ to get course details in Pydantic AI?

Use getlearningcoursedetails to fetch specific course info. Pydantic AI validates the structure of the course data before your agent uses it.

Use Pydantic AI for type-safe MSAAQ interactions, ensuring every course and student data point is validated at runtime.

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Connect MSAAQ MCP to Pydantic AI

Create your Vinkius account to connect MSAAQ 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 MSAAQ with Pydantic AI

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Runtime validation for MSAAQ enrollments

Every response from `enroll_student_in_course` passes through your Pydantic model definitions. If the server returns malformed data, your agent catches the issue before processing continues. This approach ensures that your student enrollment data stays consistent. You avoid the silent errors that plague standard API integrations by enforcing strict schemas for every tool call.

Structured student reporting in Pydantic AI

Retrieve analytics using `get_course_performance_stats` and map the output directly to your Python objects. You get full type safety for every performance metric returned. Use `list_student_certificates` to pull earned credentials into your agent's state. Because you define the schema, you can trust the certificate data before displaying it to the end user.

Managing admin data with strict types

Pull user lists with `list_registered_users_admin` and validate them against your expected structure. This keeps your administrative operations clean and predictable. Check the system health using `check_api_connectivity` as a pre-flight requirement. Your Pydantic AI agent will fail gracefully if the connection isn't ready for data exchange.

Setup guide

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

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

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

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Common questions about MSAAQ MCP in Pydantic AI

Install the slim package and use `MCPToolset` to connect. Add the toolset to your agent, and Pydantic AI will handle the schema validation automatically.

Yes. If an MSAAQ tool returns data that doesn't match your Pydantic models, the agent stops execution and reports a validation error immediately.

The server isolates all user data behind an auth token. Your agent only sees the fields you explicitly model in your Pydantic schemas.

Use `get_learning_course_details` to fetch specific course info. Pydantic AI validates the structure of the course data before your agent uses it.

Your agent will raise a validation error because of the strict Pydantic models. This prevents corrupted student records from entering your application logic.

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