Does this tool automate actual user interface tests on websites?

No. TestMonitor is essentially a Test Management Hub. You document your cases, requirements, runs, and defect reports structurally. The MCP server reads this deep structural data allowing the AI to help correlate bugs to requirements and plan sprints—it does not inherently control a web-browser headless tester to run things.

Which ID should I pass to fetch Test Runs or Cases?

Almost all context actions downstream require a `project_id`. Start by telling your prompt: 'List all my TestMonitor projects'. Take the numeric ID corresponding to your target platform, then use it as the anchor for subsequent 'list test cases', 'list runs', or 'list issues' queries.

Where do I track the Personal Access Token setting?

It is tied to your individual profile. Log in via web, go to the top right avatar, pick 'My Account', open the 'API' sub-tab, and hit 'Create Token'. Copy it precisely because it is obscured upon closing.

TestMonitor MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: TestMonitor
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect TestMonitor through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="TestMonitor Assistant",
            instructions=(
                "You help users interact with TestMonitor. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from TestMonitor"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

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IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 TestMonitor MCP Server

Link up your TestMonitor cloud infrastructure with any AI agent to streamline QA tracking operations and retrieve real-time milestone data without having to navigate web dashboards.

The OpenAI Agents SDK auto-discovers all 10 tools from TestMonitor through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries TestMonitor, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Project Triage — List all ongoing projects alongside their high-level metadata such as test coverage and delivery status
Runs & Milestones Tracking — Instantly retrieve project-scoped test runs, milestones lists, and deadline progress
Defect Auditing — Query all generated issues or software defects explicitly linked to a specific test project
Requirement Tracing — Ask the agent to map requirements against existing feature specifications without manually matching them in the UI
Team Management Lookup — Easily list out all the users provisioned in the workspace to confirm roles or debugging ownership

The TestMonitor MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 TestMonitor to OpenAI Agents SDK via MCP

Follow these steps to integrate the TestMonitor MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from TestMonitor

Why Use OpenAI Agents SDK with the TestMonitor MCP Server

OpenAI Agents SDK provides unique advantages when paired with TestMonitor through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

TestMonitor + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the TestMonitor MCP Server delivers measurable value.

Automated workflows: build agents that query TestMonitor, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries TestMonitor, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through TestMonitor tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query TestMonitor to resolve tickets, look up records, and update statuses without human intervention

TestMonitor MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect TestMonitor to OpenAI Agents SDK via MCP:

get_project_details

Retrieves details for a specific TestMonitor project

get_test_case_details

Retrieves full details for a specific TestMonitor test case

get_test_run_details

Retrieves details for a specific TestMonitor test run

list_account_users

Lists all users associated with the TestMonitor account

list_issues

Lists all issues (defects) within a project

list_milestones

Lists all milestones within a project

list_projects

Project IDs are required for most other tools. Lists all projects available on the TestMonitor instance

list_requirements

Lists all requirements for a project

list_test_cases

Lists all test cases within a specific TestMonitor project

list_test_runs

Lists all test runs within a specific project

Example Prompts for TestMonitor in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with TestMonitor immediately.

"List all TestMonitor projects."

"Get me the details for Test Case ID 5521 from project 8840."

"List all issues for Project 8840."

Troubleshooting TestMonitor MCP Server with OpenAI Agents SDK

Common issues when connecting TestMonitor to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

TestMonitor + OpenAI Agents SDK FAQ

Common questions about integrating TestMonitor MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.