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rsi-search-pro-mcp

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pdf_discover

List every PDF link on a web page with its anchor text, enabling selection of specific PDFs by name for structured data extraction.

Instructions

List every PDF link on an HTML landing page, with its anchor text.

Use this on HUB pages — PPAC consumption / production / imports, RBI
bulletin month index, MoSPI press-release listings, MoRTH notification
indexes, MCA filing pages — where the actual data lives in attached
PDFs and the page often has Year/Month/Product dropdowns that are
really just client-side filters over the same anchor set. Returns
each PDF's absolute URL and the human-readable anchor text so you can
pick by name (e.g. "Domestic Consumption of Petroleum Products-2026-27",
"Flash Report May 26").
Workflow: pdf_discover → pick by anchor text → pdf_fetch_structured.

Args:
    url: The HTML landing page URL.
    link_text_filter: Optional case-insensitive substring; only anchors
        whose text contains it are returned. E.g. "2026-27", "Flash".
    max_links: Cap on links returned (default 40).

Returns:
    {url, domain, pdfs: [{href, text, label_hint}], count,
     page_title, fetched_at}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
link_text_filterNo
max_linksNo
Behavior4/5

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

Discloses that the tool returns PDF URLs and anchor text, notes client-side filters, and explains the return structure. No annotations provided, so description handles burden. Lacks mention of error cases or rate limits, but adequate.

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?

Well-structured with sections for description, workflow, args, and returns. Each sentence is informative; no 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 description provides a clear return structure and workflow context. Moderate complexity tool; description is complete enough for an AI agent to use correctly.

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 has 0% coverage, but description explains each parameter in detail with examples (e.g., link_text_filter with '2026-27', max_links default 40), adding significant meaning beyond the schema.

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?

Clearly states 'list every PDF link on an HTML landing page, with its anchor text' and provides specific examples of hub pages, distinguishing it from siblings like pdf_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 Guidelines5/5

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

Explicitly says 'Use this on HUB pages' with concrete examples and outlines a workflow (pdf_discover → pick by anchor text → pdf_fetch_structured), guiding when to use and what to do next.

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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