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pdfdotco

PDF.co MCP Server

Official
by pdfdotco

image_to_pdf

Convert JPG, PNG, or TIFF images to PDF via URLs. Supports multiple files to combine into a single PDF document.

Instructions

Convert various image formats (JPG, PNG, TIFF) to PDF.
Ref: https://developer.pdf.co/api/pdf-from-image.md
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Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to the source file (JPG, PNG, TIFF). Multiple files are supported (by providing a comma-separated list of URLs). Supports publicly accessible links including Google Drive, Dropbox, PDF.co Built-In Files Storage. Use 'upload_file' tool to upload local files.
nameNoFile name for the generated output. (Optional)
pagesNoComma-separated page indices (e.g., '0, 1, 2-' or '1, 3-7'). Use '!' for inverted page numbers (e.g., '!0' for last page). Processes all pages if None. (Optional)
api_keyNoPDF.co API key. If not provided, will use X_API_KEY environment variable. (Optional)
httppasswordNoHTTP auth password if required to access source url. (Optional)
httpusernameNoHTTP auth user name if required to access source url. (Optional)
Behavior1/5

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

With no annotations, the description carries the full burden. It only states a generic conversion action and omits critical behavioral traits such as asynchronous execution (evidenced by sibling wait_job_completion), how multiple files are handled, or any authorization requirements beyond the api_key parameter. No side effects or constraints are disclosed.

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

Conciseness4/5

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

The core description is a single, front-loaded sentence that is clear and efficient. The appended reference link provides optional documentation but is not directly actionable for an AI agent; however, it is not verbose enough to hurt the overall structure.

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?

For a 6-parameter tool with no annotations and no output schema, the description is too sparse. It fails to mention important operational details like whether the conversion is asynchronous, whether multiple images are merged into one PDF, or what the return value looks like. The basic purpose is clear, but the overall context is incomplete.

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% with detailed parameter descriptions. The tool description adds no unique parameter insights; it merely restates the formats that align with the url parameter. Baseline of 3 is appropriate since the schema carries the semantic weight.

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 'Convert' and clearly identifies the resource 'image formats (JPG, PNG, TIFF) to PDF', which directly distinguishes it from sibling conversion tools like document_to_pdf or pdf_to_image. The format list narrows scope effectively.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives such as document_to_pdf or html_to_pdf. The description lacks any scenario-based context or exclusions, leaving the agent to infer usage solely from the tool name.

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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