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debugg-ai

Debugg AI MCP

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by debugg-ai

Run E2E Browser Test

check_app_in_browser

Check if a website or app task passes by having an AI agent browse and interact with it in a real browser. Supports localhost and public URLs. Use for visual QA, flow validation, or regression checks.

Instructions

Give an AI agent eyes on a live website or app. The agent browses it, interacts with it, and tells you whether a given task or check passed. Works on localhost or any URL. Use for visual QA, flow validation, regression checks, or anything that needs a real browser to verify.

LOCALHOST SUPPORT: Pass any localhost URL (e.g. http://localhost:3000) and it Just Works. A secure tunnel is automatically created so the remote browser can reach your local dev server — no manual ngrok setup, no port forwarding, no config.

SCOPE PER CALL: Keep each call to ONE focused check — a single page or a short interaction on a single screen (login, submit a form, verify a heading). For anything spanning multiple pages or long multi-step flows, split into SEPARATE calls — the remote browser agent has a ~25-step internal budget per call, and long single calls risk client-side timeouts. Example: instead of "log in, then go to settings, then update profile, then verify," make three calls: (1) log in & verify dashboard, (2) update settings, (3) verify profile change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesNatural language description of what to test or evaluate (e.g., 'Does the login form validate empty fields?' or 'Navigate to the homepage and verify the hero section loads')
urlYesURL to navigate to. Can be any public URL (https://example.com) OR a localhost/local dev server URL. For localhost URLs, a secure tunnel is automatically created — just make sure your dev server is running on that port.
environmentIdNoUUID of a specific environment to use for this test. See available environments in the tool description above.
credentialIdNoUUID of a specific credential to use for login. See available credentials in the tool description above.
credentialRoleNoPick a credential by role (e.g. 'admin', 'guest') from the resolved environment
usernameNoA real, existing account email for the target app. Do NOT invent or guess credentials — use one from the available credentials listed above, or ask the user. The browser agent will type this into the login form.
passwordNoThe real password for the username above. Do NOT guess or use placeholder passwords — use credentials from the list above or ask the user.
repoNameNoGitHub repository name (e.g. 'my-org/my-repo'). Auto-detected from the current git repo — only provide this if you want to run against a different project than the one you're in.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full behavioral disclosure. It reveals that a secure tunnel is automatically created for localhost URLs, the remote browser agent has a ~25-step internal budget, and long single calls risk timeouts. This gives the agent essential behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (LOCALHOST SUPPORT, SCOPE PER CALL) and front-loads the purpose. Every sentence adds value, and the length is appropriate for the complexity. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, no output schema, and no annotations, the description is remarkably complete. It covers capabilities, constraints (step budget, timeouts), usage patterns, and parameter guidance in natural language. It equips the agent to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds some context (e.g., localhost auto-tunnel for url, examples for description) but does not significantly enhance understanding beyond the schema. The mention of 'See available credentials in the tool description above' is slightly ambiguous as the description does not list them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Give an AI agent eyes on a live website or app' and lists specific uses like visual QA, flow validation, and regression checks. It distinguishes itself from siblings like probe_page (read-only) by emphasizing interactive browsing and task verification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool (visual QA, flow validation) and how to structure calls (one focused check per call, with examples). It also covers localhost support. However, it does not explicitly contrast with siblings like probe_page, which could help agents decide between them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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