How to Use the MaestroQA MCP in Pydantic AI

Q: How do I set up the MaestroQA MCP Server with Pydantic AI?

You use the MCPToolset class pointing to your Vinkius HTTP endpoint and pass it to the Agent constructor. This unified approach automatically registers tools like listqarubrics for your agent to use. Note that the old MCPServerHTTP class is deprecated.

Q: How do I handle async data exports in Pydantic AI?

Your agent triggers the export using requestqadataexport with validated start and end dates. Since the toolset runs asynchronously, your application can await the results and check progress using getexportdownloadlinks without blocking the main event loop.

Run type-safe QA audits using Pydantic AI to validate every MaestroQA score and ticket detail before it hits your database.

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Connect MaestroQA MCP to Pydantic AI

Create your Vinkius account to connect MaestroQA 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 MaestroQA with Pydantic AI

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Validate QA evaluations with Pydantic AI

This MCP Server exposes `get_ticket_qa_details` and `list_qa_tickets` so your type-safe agent can inspect support quality metrics without risk of data corruption. When you are running automated audits, silent data corruption is your worst enemy. If the QA platform returns an unexpected schema, your agent fails loudly instead of passing bad data downstream. This ensures that your performance metrics and grading reports remain completely accurate.

Sync CSAT scores with strict type safety

Your agent uses `push_csat_scores` and `list_qa_agents` to sync customer satisfaction metrics only after validating the payload schemas. Writing data back to your quality platform requires absolute precision to avoid broken database links. You can also use the agent to verify that the agent IDs exist before attempting a write. This runtime validation prevents broken database links and keeps your support metrics clean.

Type-safe asynchronous QA data exports

The `request_qa_data_export` and `get_export_download_links` tools let your agent manage bulk data exports with strict type validation. Exporting raw performance data over HTTP can be error-prone, but this toolset lets your agent call exports with validated date parameters. Once the export is ready, the agent parses the output through Pydantic models. You get clean, typed URLs that your data pipeline can download and process without unexpected parsing errors.

Setup guide

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

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

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

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Common questions about MaestroQA MCP in Pydantic AI

You use the `MCPToolset` class pointing to your Vinkius HTTP endpoint and pass it to the `Agent` constructor. This unified approach automatically registers tools like `list_qa_rubrics` for your agent to use. Note that the old `MCPServerHTTP` class is deprecated.

The framework will raise a validation error immediately at runtime. If a tool like `list_qa_tickets` returns a field that doesn't match your schema, the agent stops execution rather than processing corrupted data. This guarantees absolute correctness for your QA analytics.

Yes, the framework is completely model-agnostic. You can run your QA audits using local models or commercial APIs while still relying on this MCP Server to fetch ticket details via `get_ticket_qa_details`.

Your agent triggers the export using `request_qa_data_export` with validated start and end dates. Since the toolset runs asynchronously, your application can await the results and check progress using `get_export_download_links` without blocking the main event loop.

The MCP integration runs inside secure, ephemeral V8 isolates that execute only when called. The server only handles the specific evaluation rubrics, CSAT scores, and agent QA records you request. Your authentication tokens are stored securely and are never exposed in the agent's prompt context.

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