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emadklenka

mare-browser-mcp

by emadklenka

browser_debug

Collect a full debugging snapshot of the browser: current URL, title, console logs, alerts, and network requests with headers/body. Filter by URL, method, or console type to pinpoint issues.

Instructions

PREFERRED DEBUGGING TOOL. Returns current URL, page title, console logs, dialogs (alert/confirm/prompt), and rich network requests in one call. Always call this before browser_screenshot. Network entries include: method, URL, query params (parsed), request body (JSON/form), request headers (auth masked), status code, response body (JSON), and duration_ms for performance analysis. Use url_filter and method_filter to focus on specific API calls. Use console_types to filter log levels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
last_nNoReturn last N entries (default 50)
url_filterNoFilter network requests by URL substring
console_typesNoFilter console by type: error, warning, log, pageerror
method_filterNoFilter network requests by method e.g. POST, GET
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses detailed network entry structure, including auth-masked headers and duration_ms, which goes beyond a generic 'get debug info' description. However, it does not mention potential state impacts or whether console logs are cumulative, leaving minor gaps.

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 compact at about five sentences, with essential information front-loaded: it opens with 'PREFERRED DEBUGGING TOOL' and the rule to call before browser_screenshot. Each sentence adds value, and there is no redundancy or fluff.

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?

Despite the lack of an output schema, the description sufficiently explains the return values: URL, title, console logs, dialogs, and network request fields (method, URL, query params, headers, status, response body, duration). It also covers filter usage, making it complete for an agent to select and invoke correctly.

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?

The schema already describes all four parameters with 100% coverage, so the baseline is 3. The description adds contextual purpose for filters ('focus on specific API calls') and clarifies console_types usage, providing value beyond the schema without duplicating it.

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 explicitly identifies the tool as the 'PREFERRED DEBUGGING TOOL' and enumerates its return payload: current URL, page title, console logs, dialogs, and network requests. This distinguishes it from siblings like browser_screenshot and browser_navigate, making its purpose and scope clear.

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?

It states 'PREFERRED DEBUGGING TOOL' and 'Always call this before browser_screenshot,' providing explicit when-to-use and sequencing guidance. It also advises using specific filters to focus API calls, which is clear usage context.

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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