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Browser Extract Tables

browser_extract_tables

Extract table data from web pages into row arrays, minimizing token consumption for browser automation tasks.

Instructions

Extract content as row arrays (token-cheap vs HTML).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tabNo
sessionNodefault
limit_rowsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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 mentions 'token-cheap vs HTML' which hints at output size behavior, but it doesn't disclose what happens with malformed tables, nested tables, or whether the tool modifies the browser state. It also doesn't clarify if the extraction is read-only or if it might affect the page. The description is too brief to provide meaningful behavioral transparency.

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 a single sentence, extremely concise and front-loaded with the core action. The parenthetical 'token-cheap vs HTML' adds a useful efficiency hint without bloat. It earns its place, though it could be slightly more informative without losing conciseness.

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

Completeness2/5

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

The tool has an output schema (not shown in detail) and 3 optional parameters, but the description is too sparse to be complete. It doesn't explain the meaning of the parameters, the exact structure of the row arrays, or how it handles edge cases like tables with headers, merged cells, or multiple tables on a page. For a tool with no annotations and 0% schema coverage, this is a significant gap.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It doesn't explain what 'tab', 'session', or 'limit_rows' mean. 'limit_rows' is somewhat inferable from the name, but 'tab' and 'session' are ambiguous without context. The description adds no parameter-level meaning beyond the schema's bare names and defaults.

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 states a specific verb and resource: 'Extract <table> content as row arrays'. It clearly identifies what the tool does and the output format. It doesn't explicitly distinguish from sibling tools like browser_extract_text or browser_extract_html, but the focus on tables and row arrays is specific enough to differentiate it from most siblings.

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 implies usage: use this when you need table content in a token-cheap row-array format. It doesn't explicitly state when not to use it or name alternatives like browser_extract_html for full HTML or browser_extract_text for general text. The 'token-cheap vs HTML' hint provides some context for choosing it over HTML extraction, but it's not explicit.

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