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capture_console

Captures all console messages during page load, including uncaught exceptions, and returns structured results with counts by level for debugging runtime issues.

Instructions

Capture all console messages (log, warn, error, info, debug) during page load. Navigates to the URL, listens for console output and uncaught exceptions, then returns structured results with message counts by level. Useful for debugging runtime issues, detecting warnings, and finding errors that only appear in the browser console.

Note: Console messages may contain sensitive data (tokens, user info, etc.) — the output is returned unfiltered.

This tool is FREE — runs entirely within Claude Code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the page to capture console logs from (e.g., http://localhost:3000)
waitMsNoTime in ms to wait after page load for additional console messages (default 3000)
Behavior4/5

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

With no annotations, the description takes on the full burden. It discloses that the tool navigates to the URL, listens for console output and uncaught exceptions, returns structured results with counts, and warns that output may contain sensitive data unfiltered. It also notes it is free and runs within Claude Code, adding useful context.

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

Conciseness4/5

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

The description is three paragraphs: first explains purpose and behavior, second warns about sensitive data, third notes it is free. It is front-loaded with the key action and value. Some sentences could be consolidated without losing meaning, but overall it is efficient.

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

Completeness4/5

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

Despite lacking annotations and an output schema, the description covers the tool's behavior, input parameters, data sensitivity, and pricing. It does not detail the exact output structure beyond 'message counts by level', but the context is sufficient for an agent to understand usage and risks.

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?

Input schema has 100% coverage with descriptions for both parameters (url and waitMs). The tool description explains the overall process but does not add significant detail beyond the schema. The note about sensitive data is relevant but not parameter-specific. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool captures all console messages during page load, specifying message types (log, warn, error, info, debug). It effectively differentiates from siblings like 'capture_errors' by emphasizing the full console output, though it does not explicitly contrast with sibling tools.

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

Usage Guidelines3/5

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

The description says the tool is 'useful for debugging runtime issues, detecting warnings, and finding errors that only appear in the browser console,' implying usage context. However, it does not provide explicit when-not-to-use guidance or mention alternative tools for similar tasks.

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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