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rsi-ai-platform

rsi-search-pro-mcp

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extract

Visit a URL and extract structured data with a focus on specific information, using both text and screenshot to capture numbers from charts and SVGs 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
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the key behavior: sending rendered text and screenshot to Sonnet, and default full_page_screenshot=True to capture charts below fold. However, it does not mention other behavioral aspects like error handling, rate limits, or destructive effects, though the tool appears read-only.

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 concise: a three-sentence introduction followed by bulleted Args and Returns sections. Every sentence provides value, front-loading the purpose and unique selling point. No filler or wasted words.

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 no output schema, the detailed Returns section adequately explains the return shape. All parameters are described. The description covers the key behavioral aspect (screenshot for canvas) and references sibling tools for structure consistency. It could be improved by mentioning potential failure modes or timeouts, but overall is sufficient for a tool of this complexity.

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?

Schema description coverage is 0%, but the description fully compensates by explaining all four parameters: url, focus (with example), wait_for_selector (with reference to visit), and full_page_screenshot (explaining default True for below-fold content). This adds significant meaning beyond the schema's bare names.

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 clearly states the tool's purpose: 'Visit a URL → focused Sonnet structured extraction.' It explains it sends both rendered text and screenshot to Sonnet, enabling extraction of canvas/SVG content not in DOM. It distinguishes itself from siblings like web_fetch_structured by highlighting this unique capability, and notes the returned shape is the same as pdf_fetch_structured/web_fetch_structured.

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

Usage Guidelines4/5

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

The description implicitly guides usage by explaining the screenshot feature for canvas/SVG numbers, but does not explicitly state when to use this tool versus alternatives like web_fetch_structured. It provides clear context for when the tool is beneficial but lacks explicit exclusion conditions or alternative tool names.

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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