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pdf_page_rasterizer_highres

Rasterize complex vector PDF pages into crisp 300 DPI antialiased WebP/PNG images for vision LLMs, eliminating text clipping.

Instructions

Rasterizes complex vector PDF pages into crisp, 300 DPI antialiased WebP/PNG images optimized for multi-modal vision LLMs with zero text clipping. (0.035 USDC on Base L2)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYesInput parameters or JSON string payload for the tool execution
paymentSignatureNoBase L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the output format, resolution, anti-aliasing, and a cost of 0.035 USDC on Base L2, which gives some insight into the payment requirement. However, it does not explain how to provide the payment signature, whether paymentSignature is mandatory, or what happens on failure (e.g., invalid PDF, payment rejection). The description adds value but lacks depth on transactional and error behavior.

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 a single, information-dense sentence followed by a parenthetical cost note. It front-loads the primary purpose and includes key details (resolution, format, anti-aliasing, target use case) without any wasted words. The structure is efficient and easily scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a payment aspect and an opaque payload parameter, yet the description does not explain how to construct the payload (e.g., file path, base64, URL) or which page(s) to rasterize. It also omits any return value specification, since there is no output schema. The cost is mentioned but the payment mechanism and requirement are not clarified. These are significant gaps that could prevent an agent from calling the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100% for both parameters (payload and paymentSignature), so the baseline is 3. The description does not add any additional meaning to the parameters beyond what the schema states. The payload parameter is described generically as 'Input parameters or JSON string payload for the tool execution,' which does not specify how to pass the PDF content or page selection. The description does not compensate for this ambiguity, so it remains at the baseline.

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 a specific verb (rasterizes), a resource (complex vector PDF pages), and the output characteristics (300 DPI antialiased WebP/PNG images). It also distinguishes itself from sibling tools like convert_svg_to_png and compress_image_webp by focusing on high-resolution PDF rasterization for vision LLMs. The purpose is unambiguous and immediately differentiable.

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 provides clear context on when to use this tool: for high-quality rasterization of complex PDF pages into images suited for multi-modal vision LLMs. It implies the need for high resolution and zero text clipping, but it does not explicitly mention alternatives or conditions when not to use it. Since there are no exclusions or named alternative tools, it falls short of a 5 but clearly guides usage.

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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