How to Use the ContextQA MCP in Pydantic AI

Q: How do I initialize the ContextQA MCP Server using the new Pydantic AI syntax?

Install pydantic-ai-slimmcp and import MCPToolset. Pass your Vinkius HTTP endpoint to the toolset constructor and include it in your Agent's toolsets list. Note that the old MCPServerHTTP class is deprecated.

Secure your ContextQA test pipelines with Pydantic AI for type-safe, validated test execution and zero silent failures.

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Connect ContextQA MCP to Pydantic AI

Create your Vinkius account to connect ContextQA 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 ContextQA with Pydantic AI

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Type-safe test execution with Pydantic AI

`trigger_run` dispatches live testing commands, and Pydantic AI validates the resulting job status against strict Python schemas at runtime. If the test runner returns an unexpected payload structure, the agent halts immediately. This strict validation prevents your agent from acting on malformed run data. You get reliable, predictable test triggers without worrying about silent API failures.

Validate project and environment mappings

`get_project` and `list_projects` retrieve explicit project mapping UUIDs that your Pydantic AI agent validates against your internal configuration schemas. The agent ensures that execution spaces conform exactly to your team's tracking standards. By using `list_environments`, the agent checks target layers and mapping limits. Because every field is typed, your agent never attempts to deploy to a non-existent environment.

Extract and verify test suite structures

`list_suites` and `list_api_tests` extract asynchronous GUI test payloads and REST configurations directly into Pydantic models. Your agent parses these schemas to verify that every test suite matches your API specifications. Using `get_case` and `list_cases`, the agent validates data science object extraction boundaries. It enforces strict data validation on the test cases themselves, catching structural errors before execution.

Setup guide

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

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

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

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Common questions about ContextQA MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and import MCPToolset. Pass your Vinkius HTTP endpoint to the toolset constructor and include it in your Agent's toolsets list. Note that the old MCPServerHTTP class is deprecated.

The agent will fail loudly with a validation error instead of processing corrupted data. Pydantic AI enforces type safety on every tool response, including complex outputs from `list_executions`.

Yes. You can define a Pydantic model for your test parameters and have the agent validate the input data before calling `trigger_run`. This guarantees that only valid jobs are sent to your pipelines.

No. While the server must run externally, Vinkius hosts and manages the MCP Server for you. Your Pydantic AI agent simply connects to the hosted endpoint using your secure token.

All interactions with your project UUIDs and test suites are isolated inside ephemeral V8 sandboxes. Vinkius secures your credentials with single-token authentication, meaning your raw test definitions are never logged or stored.

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