MCP servers are used as a bridge between LLMs and QA tools like Jira, Jenkins, and TestRail, enabling AI to directly fetch logs, check builds, and log defects. This improves daily QA by automating routine checks, speeding up root-cause analysis, and reducing manual effort in defect tracking and reporting.

Requirement Analysis and Understanding 

Create Automation 

Not yet using MCP Servers

I am using MCP from tools like JIRA, Github from user story and source code context perspective. And also as a part of QA where validation happening in Kibana, Splunk, Looker - in process to explore MCP from these. This is really a start I can say, but see a lot of value using MCP to have private context.

Go through requirement and give generate testcase document

MCP = Model Context Protocol servers – middleware layer that connects AI models/tools with company data and workflows.

Test Automation Frameworks, i am using it for Automation of Mobile, Web and Desk app.

I have created MCP to make a bridge in between the Cursor AI and Android studio or Playwrite tool to make automation.

Using MCP Server with Playwright  for testing  and also for Testcase Creation 

Current Automation Framework for Speeding up Automation process and general QA process to speed up

For requirement analysis, build manual test scenarios and use cases, and then automation test cases and suites.

MCP servers are used to connect AI with company tools like Jira, TestRail, GitHub, CI/CD, and monitoring systems.

For QA, they improve daily activities by quickly pulling test logs/bug data for root cause analysis, suggesting new test cases based on recent defects, Prioritizing high-impact bugs, auto-generating test reports.

• Less switching between tools: Instead of jumping between Jira, GitHub, Jenkins, and logs, MCP pulls everything together in one place.

• Faster test writing: It can suggest or generate test cases for new features based on code changes.

• Smarter test runs: Runs only the tests needed for a specific change, saving time and cost.

• Better flakiness checks: It spots patterns in failing tests and links them to system issues, so you can fix root causes faster.

• Reliable test data: Sets up customer accounts or transactions automatically, so tests don’t break due to missing data.

Not using the MCP servers yet.

I’m not using MCP servers directly at the moment, but I’ve started hearing discussions in my team around integrating Playwright with MCP to speed up automation. From what I understand, MCP can help distribute and parallelize execution across multiple environments, which means test runs could become significantly faster and more stable.

Not using just now (plain LLMs, developer IDEs AI plugins etc.).
Future -
(1) replacement of REST APIs (& other web services) for accepting info then processing & replying.
(2) acting as a colleague beside you to aid brainstorming, housing information or expert opinions (expert in certain area chatbot models hosted on MCP server).

(3) rather than downloading & installing apps, using MCP inspector to find an agent which performs the action, once. No admin rights needed, no delay or multi-screen wizards for installing.

We use MCP servers primarily in our QA environments to support automated testing, CI/CD pipelines, and test data management. Their scalability and consistency help us run parallel tests efficiently, reduce execution time, and ensure reliable results across environments. MCP also enhances monitoring, security, and compliance, leading to faster releases and improved overall QA performance

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MCP Servers provide a standardized method for linking AI with enterprise tools, testing frameworks, and internal systems. In QA, companies generally implement MCP servers in the following areas:

Test Automation Integration: Links AI with test automation tools (Selenium, Cypress, Playwright, Appium). Dynamically generates, validates, and optimizes test cases/scripts based on requirements or user stories.

Performance Engineering:  Connects with JMeter, LoadRunner, or k6 via MCP endpoints. Automates the creation of workload models and the synthesis of performance reports.

Test Data Management Integrates with databases, synthetic data generators, or masking tools. Enables AI to securely request and provision test data in real-time.

Defect Triage & Reporting Connects to Jira, Azure DevOps, ServiceNow. AI can classify, prioritize, and automatically suggest solutions for defects.

Observability & Monitoring Links AI to monitoring systems (Grafana, ELK, CloudWatch).

Not yet… right now our MCP servers are like that fancy treadmill people buy and then use only to hang clothes on. 😅 We know it’ll help us run faster in QA someday, but for now it’s just standing there, looking impressive.

Where exactly are you using MCP Servers in your companies→

1)for integrating with AI Chatbots and AI Agents for integrating with external tools and services  

2)developing custom MCP servers to expose functionality to AI Chatbots and AI Agents

How its going to improve your daily QA activity improvements ->

1)improving quality and productivity of QA automation by integrating MCP servers both on QA developer as well as in CI/CD pipelines

2)provide easy and power 3rd eye to assist QA developers

In my experience, MCP Servers really help make daily QA work smoother and quicker. For example, instead of everyone running tests on their own local machines or spinning up dozens of browser sessions, our whole team can use shared environments on the MCP Server. This makes it easier to collaborate—if someone finds a bug while running an automated test, others can jump in and check it out right away.

It’s also great for remote debugging. If a test crashes during a CI/CD pipeline run, I can log into the server and see exactly what’s going on, rather than waiting for logs or rerunning everything. Plus, since MCP keeps all the data in sync for experiments and user sessions, it’s easier to track what’s working or needs fixing, and everyone can see the same results at the same time.

Overall, it just saves a lot of hassle, speeds up our test cycles, and brings the team together—especially when we’re pushing new changes or trying to solve tricky issues.

Hello, 

I am trying using MCP server especially the Playwright with the GPT,3.5 model.

Still i am unable to get test consistent.

I tried refining the Prompt but still not perfect.

On the other way, i tried the same test on claude4 desktop + Playwright MCP, the test passed successfully.

Thanks.

At present, we are not using MCP Servers in our organisation, obviously planning the same in near future. MCP servers will significantly improve our daily QA activities by providing unparalleled flexibility and resilience for our testing environments. We can dynamically scale resources across different cloud providers, optimizing costs and accelerating performance testing. This multi-cloud approach minimizes downtime, ensuring continuous testing even during outages. It also allows us to avoid vendor lock-in, giving us access to the best-of-breed tools and services from various providers. Ultimately, MCPs empower our QA team to deliver higher quality software faster and more reliably

I am not using yet, but I think we can create an MCP to centralize and create standards to make things happen inside and across teams.

We’re starting to look at MCP Servers as a bridge between QA tools and environments especially in impact analysis, test data resets, and regression prioritization. The real win for us is reducing manual overhead and giving testers more time to focus on finding defects rather than chasing environments or repetitive tasks. We are evaluating on how this shifts daily QA into more value-driven activities!

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I have a PoC of MCP servers to improve testing our E2E testing, in which it visits our product page and generates testing cases, then we validate them. It can helps and design tests in a form of a exploratory testing.

Thanks you All for creating such a good question-Where exactly are you using MCP Servers in your companies and how do you think, its going to improve your daily QA activity improvements 

​@Mustafa  ​@PolinaKr  ​@executeautomation 

Before proceeding , lets first understand MCP:

 A powerful solution to allow LLMs to talk to External Data Sources like GitHub, Postgres, Selenium, Rest APIs and It is an open source protocol which provide standard way of communication with data sources available locally or remotely.
 

Using MCP (Model Context Protocol) servers in our company is all about bridging the gap between AI and real-world tools, making QA smarter and faster.

Here’s how we’re using them and why they’ll supercharge your daily QA work—especially with Playwright!

Where We Use MCP Servers:

Think of MCP as a "bridge" letting AI talk to your tools, data, and systems. In QA, we use it for:

1. Automated Test Setup & Teardown:

-Before running Playwright tests, MCP fetches fresh test data (e.g., user accounts, product listings) from our databases/APIs.

- After tests, MCP cleans up leftover data so tests don’t interfere with each other.

2. Backend Validation:
   Instead of only checking the UI (like Playwright does), MCP lets us:  
   - Query databases to verify data was saved correctly.  
   - Check API responses for edge cases Playwright might miss.  

3. Real-Time Issue Debugging:
   If a test fails, MCP instantly pulls logs, error messages, or environment configs to tell us why -no manual digging required.

4. Cross-Tool Integration:

   Connect Playwright to tools like Jira (for bug tracking) or Slack (for alerts) via MCP, so tests auto-report failures without extra code.

𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬 𝐰𝐢𝐭𝐡 𝐋𝐋𝐌 (𝐨𝐫 𝐀𝐈 𝐭𝐨𝐨𝐥)?

Using MCP, MCP servers can be created which provides integration with data sources and these MCP servers can be accessed from MCP clients via MCP protocol

So MCP Clients like Claude/Gemini (LLMs) can talk to MCP servers by first setting the connection between client and server

Once, connection is established, then MCP Client (or LLMs) can talk to data sources via MCP.
𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐨𝐟 𝐌𝐂𝐏:

𝟏. 𝐌𝐂𝐏 𝐇𝐨𝐬𝐭𝐬: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
𝟐. 𝐌𝐂𝐏 𝐂𝐥𝐢𝐞𝐧𝐭𝐬: Protocol clients that maintain 1:1 connections with servers
𝟑. 𝐌𝐂𝐏 𝐒𝐞𝐫𝐯𝐞𝐫𝐬: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
𝟒. 𝐋𝐨𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: Your computer’s files, databases, and services that MCP servers can securely access
𝟓. 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: External systems available over the internet (e.g., through APIs) that MCP servers can connect to.

How MCP works in practice (step-by-step):

1. Query: A user gives a prompt to an MCP-compatible app.
2. MCP Client: Receives the prompt and manages communication between the model, tools, and final output.
3. LLM: Sends the query to an LLM (e.g., GPT‑4,GPT5, Claude, DeepSeek), which interprets the task, plans steps, and selects tools from available schemas.
4. Tool Selection: The LLM instructs the client which MCP server to call and which function to invoke (typically via JSON).
5. Request: The client sends the request (function name + parameters) to the chosen MCP server using its OpenAPI-compatible schema.
6. Execution: The MCP server routes it to the right tool or service (e.g., Slack, GitHub), validating inputs with JSON Schema.
7. Response: The tool’s response returns to the client, then the LLM, which may summarize or format it for the user

How MCP Improves Daily QA (Especially with Playwright!)

Playwright is great for UI testing, but MCP adds superpowers:

Why This Matters for You (Playwright User)

- Less Frustration: No more works on my machine bugs—MCP gives you the full picture.  
- Faster Releases: Tests run quicker (backend checks are instant) and catch more bugs early.  
- Confidence: Real data + real logs = bulletproof tests.  

-Getting Started , few pointers need to considered:

If you’re new to MCP, start small:  
1. Set up an MCP server for your test database (e.g., SQLlite).  
2. Write a Playwright test that:  
   - Uses MCP to create a test user.  
   - Runs your UI flow (e.g., login).  
   - Uses MCP again to verify the user exists in the DB.  

---Basically best practise you can download claude server and integrate what you like selenium or plawright or also in vs code you can also directly integrate your mcp server and its give good result and streamline  your code.

You’ll see immediate gains in speed and reliability!