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debugg-ai

Debugg AI MCP

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Probe Page

probe_page
Read-only

Probe one or more URLs to get their rendered state, including screenshots, metadata, console errors, and network summary. Ideal for smoke-testing routes after code changes.

Instructions

Probe one or more URLs and return their rendered state — screenshot, page metadata (title/finalUrl/statusCode/loadTimeMs), structured console errors, and per-URL network summary (refetch loops collapse into one row by origin+pathname).

WHEN TO USE: "did I just break /settings?" / "smoke-test these 5 routes after my refactor" / "what's actually rendering at /dashboard?" — fast (<10s for 1 URL, <25s for 20), no LLM cost, no agent loop.

NOT FOR: scenario verification (sign in → click X → assert Y), interaction (clicks, form fills, scrolls), or anything requiring agent decisions. Use check_app_in_browser for those.

LOCALHOST SUPPORT: any localhost URL is auto-tunneled. Pre-flight TCP probe fails fast (<2s) if the dev server isn't listening.

BATCH MODE: pass up to 20 targets in one call to share browser session + tunnel — dramatically faster than firing parallel single-URL probes (one execution unit, not N). Per-URL waitForSelector / waitForLoadState / timeoutMs override defaults.

A single failed target's error appears in result.error without failing the whole batch — the other results stay valid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetsYes1-20 URLs to probe. Each entry can carry its own per-URL wait config.
repoNameNoGitHub repository name (e.g. 'my-org/my-repo'). Auto-detected from the current git repo — only provide this to scope the probe to a different project context.
includeHtmlNoIf true, each result includes the page's outerHTML. Default false to keep response size sane.
captureScreenshotsNoIf true (default), one PNG screenshot is returned per target. Set false for very large batches or when only the structured data matters.
Behavior5/5

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

Annotations provide readOnlyHint and openWorldHint. Description adds substantial behavioral detail: batch sharing, auto-tunneling, fail-fast TCP probe, per-URL wait overrides, partial failure handling. No contradiction.

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?

Well-structured with clear sections (WHEN TO USE, NOT FOR, LOCALHOST SUPPORT, BATCH MODE), front-loaded with core purpose, every sentence adds value, 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 4 params, full schema coverage, no output schema, description fully explains the output format and batch behavior. Complete for correct agent usage.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining batch behavior, defaults (timeout 10000, waitForLoadState 'load'), context for setting captureScreenshots false, and auto-detection of repoName.

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?

Description clearly states the tool probes URLs and returns rendered state (screenshot, metadata, console errors, network summary). It distinguishes from siblings: check_app_in_browser for interaction, trigger_crawl for crawling.

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

Usage Guidelines5/5

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

Provides explicit WHEN TO USE (quick smoke tests) and NOT FOR (scenario verification, interaction). References sibling tool check_app_in_browser for those cases. Also discusses batch mode and localhost support.

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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