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PDF Text Extractor for AI Agents

industrial_platform/pdf-text-intelligence

industrial_platform--pdf-text-intelligence
Destructive

This tool calls the Actor "industrial_platform/pdf-text-intelligence" and retrieves its output results. Actor description: Extract embedded PDF text, pages and metadata. Price: $0.0015 per successful PDF.

This tool requires an x402 payment. Include a valid x402 payment signature in the request metadata (_meta["x402/payment"]). Your MCP client must support the x402 payment protocol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes**REQUIRED** Public HTTP/HTTPS PDF URLs to process. Up to 50 unique PDFs per run. Example values: ["https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"]
waitSecsNoMax seconds (0–45, default 30) to cap the wait for the Actor run to reach terminal state. For long-running Actors the response returns at the cap with the current run status; follow `nextStep` to poll via get-actor-run. Set to 0 to fire-and-forget.
max_pagesNoMaximum pages extracted from each PDF. Example values: 200
concurrencyNoMaximum number of PDFs downloaded and parsed concurrently. Example values: 5
max_text_charsNoMaximum retained text characters per PDF. Example values: 500000
timeout_secondsNoMaximum time allowed to download each PDF. Example values: 45

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations declare openWorldHint=true, readOnlyHint=false and destructiveHint=true, and the description adds genuinely non-derivable behavior: an x402 payment is required, the cost is $0.0015 per successful PDF, and the payment signature must be placed in _meta. That is exactly the kind of auth/billing context annotations cannot express.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The most valuable content (payment requirement and signature placement) is buried after boilerplate 'calls the Actor and retrieves its output results' text that merely restates the name. It is not bloated, but it is not front-loaded around what the agent most needs to act on.

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?

An output schema exists, so return values need no explanation, and the description covers the payment gate, per-PDF pricing, and wait/polling semantics. Minor gaps remain, such as failure/refund behavior on unsuccessful PDFs, but nothing essential to correct invocation is missing.

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

Parameters4/5

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

Schema description coverage is 100%, so the six documented parameters already carry their own semantics and the baseline would be 3. The description goes beyond the schema by disclosing a hidden, undocumented input channel (the required x402 payment signature in _meta), which is real added value for correct invocation.

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 the concrete operation: it calls the 'industrial_platform/pdf-text-intelligence' Actor and retrieves its output, and the embedded Actor description specifies the resource precisely (embedded PDF text, pages, metadata). An agent can distinguish it from generic siblings like get-dataset-items or get-actor-run, though the framing is boilerplate wrapper language rather than a direct verb+resource statement.

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

Usage Guidelines2/5

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

No guidance on when to choose this tool over the sibling run/dataset tools, nor on prerequisites such as URL eligibility beyond what the schema already says. The only conditional information given is the polling hint in waitSecs, which is parameter-level, not tool-selection guidance.

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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