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fetch_pdf_markdown

Convert any PDF research paper or report into structured Markdown. Pay 0.05 USDC on Polygon and get clean, readable text for AI analysis.

Instructions

Extracts structured Markdown from PDF research papers and reports. Requires 0.05 USDC on Polygon.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
payment_tx_hashNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavioral traits. It adds a unique operational detail: 'Requires 0.05 USDC on Polygon,' which implies payment/authentication prerequisites not present in the schema. However, it omits other potential side effects like network calls or failure modes, so it's not a 5.

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 extremely concise—two short sentences. It front-loads the primary function in the first sentence and adds crucial cost information in the second. Every word earns its place, with no fluff or redundancy.

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?

For a simple fetch-and-convert tool, the description covers the essentials: input (PDF), output (Markdown), and a key prerequisite (payment). An output schema exists, so return details aren't needed. It lacks a bit of detail about error cases or what happens without payment, but overall it's sufficiently complete.

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 coverage is 0%, so the description should compensate by explaining the parameters. It mentions the 0.05 USDC cost, which hints at the purpose of payment_tx_hash, but it never explicitly ties the cost to that parameter or describes the url parameter. This indirect and incomplete guidance warrants a low score.

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 uses a specific verb ('Extracts') and resource ('PDF research papers and reports') with output format ('structured Markdown'). It clearly distinguishes itself from sibling tools like fetch_youtube_transcript or fetch_clean_web_content, which target different content types.

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 implies PDF-only usage and mentions a payment requirement, giving clear context. Though it doesn't explicitly list alternatives or exclusions, the sibling names and the explicit 'PDF' qualifier make the appropriate use case apparent.

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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