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  1. Home
  2. ๐Ÿ”’ Security
  3. Deskcert
Deskcert logo
Health: ActiveRecent health check succeeded.Last checked 10/11/2026, 5:03:26 PM

Deskcert

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securityweb-testingplaywrightagent-evaluationmcp

Tests AI agents on your web app with Playwright, scoring task completion and blocking forbidden actions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "deskcert": {
      "command": "npx",
      "args": [
        "-y",
        "deskcert-cli"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿ”’ More in Security

Overview

The deskcert MCP server runs agent safety suites against internal web applications through Playwright. Its stdio MCP interface exposes a `run_suite` tool, allowing deployment pipelines or orchestrating agents to invoke suite execution without shelling out to the CLI. Suites define a target URL, tasks, success checks, and forbidden actions in YAML. Use it when you need CI gates for browser-based agents operating staging, local, or VPN-protected web apps.

Use cases

โ€ขEvaluate an agent against an internal admin panel
โ€ขBlock forbidden browser actions in CI
โ€ขCompare agent behavior on a staging web app
โ€ขRun safety suites from an orchestrating agent
โ€ขValidate task completion with Playwright

Key features

โ€ขExposes a stdio MCP `run_suite` tool
โ€ขRuns browser-based suites with Playwright
โ€ขIntercepts and records forbidden actions
โ€ขSupports YAML task suites for custom web apps
โ€ขProvides CI exit codes for score and safety gates
โ€ขIncludes scripted and pluggable agent adapters

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Deskcert.

Extracted Tool Capabilities
Exposes a stdio MCP `run_suite` tool
Runs browser-based suites with Playwright
Intercepts and records forbidden actions
Supports YAML task suites for custom web apps
Provides CI exit codes for score and safety gates
Includes scripted and pluggable agent adapters

How Deskcert works

What the deskcert MCP server does

The deskcert MCP server evaluates whether an AI agent can perform approved tasks in a web application without attempting actions that the suite forbids. You author the test suite for the application under review rather than using a fixed benchmark environment. This makes it suitable for internal admin panels, dashboards, CRUD tools, and other browser-accessible systems.

Each task identifies the application through a target_url, describes the work the agent should perform, and lists named forbidden_actions. The runner checks task outcomes and applies the forbidden-action policy independently from the numeric score. An attempted forbidden action is intercepted before it reaches the page, recorded with its action name and step number, and causes the suite gate to fail.

The deskcert MCP server is scoped to web applications. It does not provide native desktop automation, VM snapshots, or operating-system-level control for Windows or macOS applications.

How it works

DeskCert drives the target site with Playwright. An agent adapter receives a screenshot and an accessibility-tree text dump, then returns the next action. The project includes a scripted adapter that requires no model or API key, while the adapter interface can be connected to an external or in-house agent loop.

The MCP mode is started with deskcert mcp and exposes run_suite over standard input and output. A deployment pipeline or coordinating agent can call that tool instead of launching the command-line runner directly. The same project also provides CLI commands for initializing suites, running them, and applying CI-specific exit codes.

For CI, exit code 0 indicates a pass, 1 indicates that the score is below the configured threshold, and 2 indicates at least one forbidden-action violation. This lets a pipeline distinguish an insufficient score from an agent attempting an explicitly blocked operation. The score reflects only the tasks and guardrails defined in the suite; it is not a general certification of the agent.

Setup and configuration

Install either package before using the deskcert MCP server:

  • npm: npm install -g deskcert-cli
  • PyPI: pip install deskcert-cli

Install the Chromium browser used by Playwright with npx playwright install chromium for the npm installation or playwright install chromium for the Python installation.

Run deskcert init to create an example suite and fixture application, or provide your own suite directory with --suite. A suite can target a local fixture, staging system, or internal application reachable through the relevant network. The schema accepts only http:// and https:// target URLs; file:// and javascript: URLs are rejected.

The Python package also includes deskcert serve-fixture, while the npm package can run the bundled fixture server directly with Node. Both package implementations expose the same init, run, ci, and mcp command surface and are expected to produce equivalent scoring results.

Tools and capabilities

The deskcert MCP server provides the run_suite MCP tool over stdio. Its surrounding CLI supports these related capabilities:

  • Create an example suite and fixture with init.
  • Run a suite with a selected agent adapter through run.
  • Apply CI pass, threshold, and forbidden-action gates through ci.
  • Start the MCP interface with mcp.
  • Intercept and record forbidden actions before page execution.
  • Score task completion while keeping guardrail violations as unconditional failures.

Suites are written in YAML and validated against the included JSON Schema. The browser runner can use the bundled scripted adapter for repeatable initial tests or CI self-tests, or an implementation of the two-method AgentAdapter interface for another agent framework.

Limitations and notes

Testing is limited to browser-driven web applications. The project does not claim to assess an agent across arbitrary desktop software or a complete operating-system environment. Results depend on the tasks, success checks, forbidden actions, and threshold selected in the suite.

A successful numeric score does not override a forbidden-action violation. Conversely, the suite score is not a broad safety rating: it describes the specific application and scenarios that were executed. The MCP transport documented here is stdio, so the calling client must be able to launch and communicate with a local MCP process.

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

npm downloads
254
Package downloads in the last 30 days.
Last commit
12d ago
Most recent push to the default branch.

Reviews

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Frequently Asked Questions about Deskcert

deskcert is an MCP server that connects an AI agent to browser-based test suites for internal web applications through Playwright. Its main MCP tool, `run_suite`, executes YAML-defined tasks, checks completion, intercepts forbidden actions, records violations, and returns results for pipeline or orchestrator decisions.

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Technical Specs & Signals

Category๐Ÿ”’Security
More technical detailsExpand โ–พ
TransportSTDIO
RuntimeNode.js
LicenseApache-2.0
ClientsClaude Desktop, Cursor, Cline / VS Code, Windsurf, Claude Code, VS Code (GitHub Copilot), Zed, OpenAI Codex CLI, Gemini CLI, JetBrains AI Assistant, Roo Code, Continue, LM Studio
Last updatedOct 8, 2026
4/4 checks healthy over the last 43d
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit12d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 29, 2026
npm downloads254/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
43Quality signal: Fair ยท 43/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity6/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev ยท deskcert-cli (npm)

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