How to Use the Cypress Cloud MCP in Pydantic AI

Q: Can I use Pydantic AI to build a dashboard from Cypress Cloud data?

Yes, and it's a great fit. Your agent can call reportruns and reporttests on a schedule. Because Pydantic AI guarantees the data structure, you can reliably pass the resulting objects to a web framework or dashboarding tool without extra parsing or error handling.

Q: What happens if the Cypress Cloud API changes and my Pydantic AI agent fails?

It fails loudly and immediately. You'll get a clear ValidationError pointing to the exact mismatch between the API response and the expected model. This is a feature, not a bug—it prevents your agent from making bad decisions based on corrupted or unexpected data from Cypress Cloud.

Get type-safe, validated Cypress Cloud data in your Pydantic AI agent. No more silent failures or unpredictable API responses.

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Connect Cypress Cloud MCP to Pydantic AI

Create your Vinkius account to connect Cypress Cloud 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 Cypress Cloud with Pydantic AI

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Get Validated Test Run Objects

Stop writing boilerplate code to parse API responses. When your agent calls `get_run`, Pydantic AI automatically validates the response against a strict schema. You get a clean, typed Python object, or a loud `ValidationError` if the data is malformed. This means you can trust the data your agent works with. If the Cypress Cloud API ever changes, your agent won't silently fail or start hallucinating fields. It will stop, tell you exactly what's wrong with the data from tools like `get_instance` or `get_tests`, and wait for a fix.

Build Reliable Reporting Agents

Build reporting bots that don't break. Use tools like `report_flaky` and `report_slow` to pull performance data. Pydantic AI ensures every field—test titles, flake rates, durations—matches the expected type and format before your agent even sees it. This correctness is critical for automation. You can build an agent that runs `report_runs`, parses the validated output, and confidently files a Jira ticket or sends a Slack alert. You know the data in that ticket will be accurate because it passed Pydantic's validation gauntlet.

Integrate Any LLM with This MCP Server

Pydantic AI is model-agnostic, so you can bring your own LLM—OpenAI, Anthropic, Gemini, or a local model. This MCP Server plugs right into that ecosystem. Your agent gets access to all the Cypress Cloud tools regardless of the brain you choose for it. The setup is minimal. Just point the `MCPToolset` to your Vinkius endpoint URL. Your agent can then start making calls like `list_projects` to find a project ID and `get_runs` to inspect its test history, with every response getting the full Pydantic validation treatment.

Setup guide

Set up Cypress Cloud 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": {
        "cypress-cloud-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Cypress Cloud MCP in Pydantic AI

When this MCP server returns data from Cypress Cloud, Pydantic AI intercepts it. It checks every field against a predefined Pydantic model. If a field is missing or has the wrong type, it raises a `ValidationError` instead of passing bad data to your agent.

Yes, and it's a great fit. Your agent can call `report_runs` and `report_tests` on a schedule. Because Pydantic AI guarantees the data structure, you can reliably pass the resulting objects to a web framework or dashboarding tool without extra parsing or error handling.

It fails loudly and immediately. You'll get a clear `ValidationError` pointing to the exact mismatch between the API response and the expected model. This is a feature, not a bug—it prevents your agent from making bad decisions based on corrupted or unexpected data from Cypress Cloud.

There's a tiny overhead for validation, but it's usually negligible compared to the network latency of the API call itself. The trade-off is a massive increase in reliability and developer confidence.

The server reads your test execution data: run statuses, test titles, instance details, and aggregated reports. It cannot write or delete anything. Pydantic AI itself doesn't see your credentials; it just interacts with the Vinkius MCP endpoint, which handles authentication securely in an isolated sandbox for each request.

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