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rsi-ai-platform

browser-research-mcp

extract

Visit a URL and extract structured data including key facts, numeric values, and tables using both rendered text and a screenshot for content not in the DOM.

Instructions

Visit a URL → focused Sonnet structured extraction.

Sends BOTH rendered text AND a screenshot to Sonnet — so numbers drawn via canvas / SVG (chart values on PPAC, RBI, NSE dashboards) that don't appear in the DOM still get extracted. Same returned shape as pdf_fetch_structured / web_fetch_structured on authority-web-search-mcp.

Args: url: The page URL. focus: What to extract, e.g. "monthly LPG, MS, HSD consumption for FY2024-25" or "Q4 FY26 EBITDA margin and revenue". wait_for_selector: Optional CSS selector to await (see visit). full_page_screenshot: Default True so charts below the fold are seen.

Returns: {url, domain, title, dateline, summary, key_facts[], numeric_values[], dates[], tables_summary[], kind: "browser"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
focusNo
wait_for_selectorNo
full_page_screenshotNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that both rendered text and screenshot are sent, enabling extraction of non-DOM numbers. However, it omits potential side effects like latency, cost, or limitations.

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 well-structured and front-loaded, but the return type listing could be slightly more concise. Still efficient and easy to parse.

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?

Given the tool's complexity (4 params, output schema), the description covers purpose, parameters, and return shape. However, it does not address error handling or comparison with siblings, leaving some gaps.

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

Parameters5/5

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

Input schema has 0% description coverage, but the description's 'Args' section fully explains each parameter with examples and defaults, adding substantial value beyond the schema.

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?

Description clearly states 'Visit a URL → focused Sonnet structured extraction', specifying the action and resource. It distinguishes by mentioning extraction of canvas/SVG content, but does not explicitly differentiate from sibling tools 'act' and 'visit'.

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 provides examples of what to extract (e.g., 'monthly LPG, MS, HSD consumption') and implies usage for focused extraction, but lacks explicit when-to-use or when-not-to-use compared to siblings.

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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