Can my agent help me debug a failed Cypress test?

Yes. Use the 'get_instance' tool with the instance ID. The agent will retrieve explicit error messages and can provide you with direct links to failure screenshots and video recordings stored in Cypress Cloud.

How do I identify which tests are flaky in my project?

Provide a start date to the 'report_flaky' tool. Your agent will analyze the historical data from Cypress Cloud and identify tests that intermittently pass and fail, helping you improve your CI reliability.

Can I use the agent to monitor the duration of my test runs?

Absolutely. The 'report_slow' tool retrieves high-precision duration metrics. The agent can surface the slowest tests by average or p95 duration, allowing you to optimize your test suite's execution time.

Cypress Cloud MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Cypress Cloud
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Cypress Cloud through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Cypress Cloud "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Cypress Cloud?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Cypress Cloud MCP Server

Connect your Cypress Cloud enterprise account to any AI agent and take full control of your end-to-end testing lifecycle and quality metrics through natural conversation.

Pydantic AI validates every Cypress Cloud tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Run Monitoring — List recent test executions for your projects and retrieve detailed passed/failed/pending counts and commit info
Instance Deep Dives — Inspect specific spec file executions to retrieve error messages, screenshots, and video URLs for failed tests
Flaky Test Identification — Generate enterprise reports to identify intermittent failures and audit last flake dates across your codebase
Performance Auditing — Retrieve slow test reports to evaluate average durations and p95 performance metrics for your CI/CD pipeline
Enterprise Reporting — Fetch aggregated run summaries and granular test result data formatted for BI dashboards and audits
Project Navigation — List all organizational projects and identify unique 6-character IDs required for programmatic data extraction

The Cypress Cloud MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Cypress Cloud to Pydantic AI via MCP

Follow these steps to integrate the Cypress Cloud MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Cypress Cloud with type-safe schemas

Why Use Pydantic AI with the Cypress Cloud MCP Server

Pydantic AI provides unique advantages when paired with Cypress Cloud through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Cypress Cloud integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Cypress Cloud connection logic from agent behavior for testable, maintainable code

Cypress Cloud + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Cypress Cloud MCP Server delivers measurable value.

Type-safe data pipelines: query Cypress Cloud with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Cypress Cloud tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Cypress Cloud and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Cypress Cloud responses and write comprehensive agent tests

Cypress Cloud MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Cypress Cloud to Pydantic AI via MCP:

get_instance

Get full details of a Cypress spec instance including spec name, status, error messages, screenshots, video URLs, and browser info

get_instances

List spec instances within a Cypress run. Each instance represents one spec file execution. Returns instance IDs, spec names, statuses, and durations

get_run

Get full details of a Cypress Cloud run including status, total tests, passed/failed/pending counts, duration, parallelization, groups, and commit info

get_runs

List recent test runs for a Cypress Cloud project. Returns run IDs, commit info, branch, CI build IDs, statuses (passed/failed/running), durations, and spec counts

get_tests

List individual tests within a Cypress spec instance. Returns test titles, states (passed/failed/pending/skipped), durations, and error messages

list_projects

Useful for finding the `project_id`. List all projects on Cypress Cloud. Cypress is the leading JavaScript E2E testing framework. Returns project names, IDs, and org info via the Enterprise Data Extract API

report_flaky

Get flaky test report from Cypress Cloud. Identifies tests that intermittently pass/fail. Returns test names, flake rates, and last flake dates

report_runs

Must provide the start date. Get enterprise run summary report from Cypress Cloud. Aggregated data for BI dashboards. Requires start_date (YYYY-MM-DD)

report_slow

Get slow test report from Cypress Cloud. Identifies slowest tests by average duration. Returns test names, avg/p95/max durations

report_tests

Get enterprise test results report from Cypress Cloud. Individual test-level data with statuses and error messages

Example Prompts for Cypress Cloud in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Cypress Cloud immediately.

"List the last 5 test runs for project 'abc123'"

"Show me why instance 'ins_789' failed"

"Give me a report of flaky tests starting from 2024-01-01"

Troubleshooting Cypress Cloud MCP Server with Pydantic AI

Common issues when connecting Cypress Cloud to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Cypress Cloud + Pydantic AI FAQ

Common questions about integrating Cypress Cloud MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Cypress Cloud MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.