Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Description from the maker (original English)
Katalon True Platform MCP: design tests, run with AI, upload reports, assess release readiness.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Works in Claude on the web, desktop and mobile:
https://platform.katalon.io/mcpRun this in your terminal
claude mcp add --transport http testops https://platform.katalon.io/mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"testops": {
"url": "https://platform.katalon.io/mcp"
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"testops": {
"type": "http",
"url": "https://platform.katalon.io/mcp"
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
ChatGPT connects only to servers on the internet:
https://platform.katalon.io/mcpMenu names can differ slightly by plan and over time.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
We have not read this server’s tool list yet; we check endpoints in rotation.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The publisher (katalon-studio) is not the repo owner (katalon-labs); someone else may have listed it
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
Not checked yet; we check endpoints in rotation
Not checked yet; we check endpoints in rotation
Not checked yet; we read the tool list when we check the endpoint
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 0 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.
The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.
In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Start here · Quickstart · Skills · Lifecycle · Install · MCP · Contributing
Ask your coding agent to read a requirement, design the test cases, run them with AI, upload the reports, and tell you whether the release is safe to ship. The skills do the platform work through the Katalon MCP, so the agent operates your real project instead of guessing at it.
An example session. Your requirement keys, suites, and verdicts come from your own workspace.
1. Install the skills. The skills CLI detects the agent you already run (70+ supported) and drops the skills into its native directory.
npx skills add katalon-labs/true-skills
2. Point it at your Katalon workspace. Add the MCP server to your agent's config file (the install section says which file yours is):
{
"mcpServers": { "katalon-prod-mcp": { "type": "http", "url": "https://platform.katalon.io/mcp" } } }
First connect opens a browser OAuth flow and lets you pick your workspace. Nothing to fill in, no tokens to paste, no keys to store.
3. Ask for something real.
Set up Katalon MCP and verify my projects.
Create manual tests from requirement CEL-6 and link them. Which requirements in this sprint have no test coverage? Run that suite with AI and summarize what broke. Generate a Playwright script from test case TC-1042. Upload my Playwright report to Katalon and verify the run. Is release 3.2 safe to ship?
Or hand over the whole chain: *"Analyze CEL-6, design and import the cases, build a suite, run it with AI, and tell me if we can ship."*
Four jobs. Say the situation in your own words and the agent picks the skill.
| You are a | You say | It starts at |
|---|
| Manual tester QA analyst, test analyst | *I just got requirement CEL-6 and need cases* · *Run this suite with AI and tell me what broke* · *Is this failure the app or my test* | create-test-cases → test-data → execute-test → analyze-failures Unscripted session? exploratory-charter. | | Automation tester SDET, automation engineer | *Turn TC-1042 into a Playwright spec* · *Get my run into the platform* · *Our suite went flaky this month* | test-case-to-playwright → playwright-execute → test-maintenance | | Test lead QA lead, QE lead | *What should we test first this sprint* · *Is this suite ready for the pipeline* · *Which requirements have no coverage* | test-plan → test-estimation → test-review → test-management | | Test manager QA manager, head of quality | *Can we ship 3.2* · *How long will this take and with how many people* · *Build the summary I present upward* | release-analyze → test-estimation → test-reporting |
Not connected yet? Start at platform-setup, which everyone runs once.
Not sure which one you are, or the request spans several? Ask true-platform-testing and it routes by role and by stage. The full map from intent to skill, including what to fall back to for the parts not built yet, is in lifecycle-map.md.
Nineteen skills, one folder each under skills/. The agent picks the right one from its description, so you rarely name a skill yourself.
| Skill | Stage | What it does |
|---|---|---|
| platform-setup | setup | Installs and verifies the Katalon MCP, and diagnoses auth or access failures. Start here. |
| get-started | setup | First-run onboarding after the connection is installed. Confirms the signed-in account, picks the default project in at most two questions, and opens the Katalon home view. The ChatGPT plugin runs it on install. |
| test-plan | 1 plan | Turns quality goals into scope, ranks the work by requirement coverage and risk, and builds the folder and suite structure that acts as the executable plan. |
| test-estimation | 1 plan | Sizes a test cycle from scope, risk, and historical execution data, resources it across the manual and automated lanes, and states the uncertainty instead of inventing precision. |
| create-test-cases | 2 design | Reads a requirement or free text, designs atomic manual cases with ISTQB techniques as reference, skips duplicates, imports only what is missing, and links each case back to the requirement. |
| test-data | 2 design | Designs the data a case runs on, decides what must exist before a run, keeps literals out of the step text, and resets state afterwards so the next run starts clean. |
| exploratory-charter | 2 design | Writes a charter with a mission, areas, oracles, and a timebox, runs the session, and turns what was found into defects and scripted cases. |
| test-management | 3 manage | Organizes folders and suites, finds assets at scale, and produces a requirement to case to suite traceability report with coverage percentage and orphans. |
| test-review | 4 review | Reviews coverage, case quality, and flakiness before anything enters the pipeline, and returns Approve, Approve with fixes, or Reject plus the specific weak cases. |
| execute-test | 5 execute | Runs a case, a list, or a suite as a manual run, a Run with AI session, or scheduled automation, then reports pass, fail, and blocked. |
| upload-report | 5 execute | Runs automation and uploads or verifies Katalon Studio/KRE, JUnit XML, and Playwright reports on the platform. |
| test-case-to-playwright | 5 execute | Converts manual cases into Playwright TypeScript with Page Object Model and fixtures. |
| test-case-to-katalon-studio | 5 execute | Converts manual cases into Katalon Studio test cases and Groovy scripts, keeping the .tc and .ts pair GUID-consistent. |
| playwright-execute | 5 execute | Runs Playwright specs, ships the report with @katalon/playwright-reporter, and verifies the run landed. |
| analyze-failures | 6 analyze | Sorts failures into product defect, automation defect, and environment noise, clusters them by signature, and files ALM defects for the real bugs. |
| release-analyze | 6 analyze | Reads coverage, stability, and defect data to return Ready, Ready with risk, or Not ready, with the reasons attached. |
| test-reporting | 6 analyze | Turns platform quality data into the few metrics that answer a stakeholder question, trended across releases, as a summary a manager can present upward. |
| test-maintenance | 7 maintain | Finds flaky and broken cases from stability history, repairs or regenerates them, and hands the refreshed gap list back to planning. |
| true-platform-testing | all | The router and end to end runner. Routes any request to the right stage, or drives the full chain from requirement to ship decision. |
Multi-skill playbooks live in combination-recipes.md. Copy-paste prompts and cross-model notes are in prompt-recipes.md.
Seven stages, each owned by a skill. The loop closes when maintenance feeds its gap list back into the plan.
platform-setup sits before all seven, connecting the MCP. Every skill states which Katalon MCP tools it uses and where the platform stops. Full stage map: lifecycle-map.md. Every tool in one line each: mcp-tool-index.md. A self-contained visual for humans: docs/lifecycle.html.
Each skill names its limits up front so the agent does not promise work the platform cannot do.
Through the MCP: list projects and repositories, read requirements, create and update and link test cases, manage suites and folders, create manual runs, start Run with AI, poll AI sessions, read results, fetch quality metrics, and file ALM-linked defects.
Not through the MCP: creating requirements, creating a formal Test Plan entity, guaranteeing an AI run finishes, or inspecting the live app UI without a browser tool. A named suite plus a release or sprint association stands in for the Test Plan entity.
Some work is a product surface rather than an API call: object capture and resilience design in Studio, custom fields and Git config and governance in the TestOps UI, self-healing and Time Capsule and TrueTest regeneration, and rerun, terminate, and Live Monitor. The full list is in unavailable-capabilities.md.
Every path needs the Katalon MCP configured.
Any agent via the skills CLI
npx skills add katalon-labs/true-skills # all skills, this project
npx skills add katalon-labs/true-skills --skill platform-setup # just one npx skills add katalon-labs/true-skills -g # user-global, every agent npx skills update # pull the latest
The CLI installs skill files only. Configure the MCP yourself with the snippet below, or use the Claude Code and Codex plugin paths, which bundle it.
Any agent via the Ag
Only the beginning is shown; read the rest in the repo