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Kamy

by dev.kamy

Get render pages as images

get_render_pages
Read-only

Rasterise every page of an existing render to a PNG image and return one signed 1-hour URL per page, with pixel width and height. Use this when a page has to be looked at rather than read — thumbnails, previews, visual QA of a layout, or an image attachment — and use extract_from_render when you want the text. The render must already have status 'success'; a queued or failed one comes back as 409 RENDER_NOT_READY. Repeated calls overwrite the same page images, so it is safe to retry. Rasterisation costs no render quota. Returns { pages: [{ page, width, height, url }], count, dpi }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dpiNoRaster resolution. Defaults to 150 (screen quality); 300 for print-quality thumbnails. Clamped to 72-300 server-side.
render_idYesRender id (UUID) as returned by render_pdf, render_async/get_job, or list_renders.

TDQS

A4.7/5.0
Behavior5/5

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

Description adds valuable behavioral context beyond annotations: it states rasterisation costs no quota, repeated calls overwrite same images (safe to retry), and returns structure with pages, count, dpi. Annotations already indicate readOnly and non-destructive, but description enriches with concrete details and error condition.

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?

Two sentences, zero wasted words. Every phrase adds value: purpose, usage guidance, prerequisite, idempotency, cost, output format. Front-loaded with key action and result. Excellent structure.

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

Completeness5/5

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

Despite no output schema, the description explicitly documents the return format (pages array with page, width, height, url, count, dpi). Covers preconditions, error behavior, idempotency, and quota. Complete for a read-only tool with clear inputs and outputs.

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%, so the schema already documents both parameters thoroughly (e.g., defaults, clamping for dpi). The description does not repeat parameter details, which is appropriate given schema richness. No additional parameter semantics beyond what schema provides, hence baseline 3.

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?

Description clearly states the tool rasterizes every page of a render to PNG images with signed URLs, and explicitly distinguishes from extract_from_render for text extraction. The verb 'rasterise' and resource 'render' are specific and unambiguous.

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 tells when to use (thumbnails, previews, visual QA, image attachment) and when not to (use extract_from_render for text). Also specifies prerequisite that render must have status 'success' and warns about 409 error. No ambiguity.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct action or resource with minimal ambiguity. For example, `render_pdf`, `render_docx`, `render_xlsx`, and `pptx` are clearly different output formats, while `merge_pdfs`, `split_pdf`, and `edit_pdf` target different PDF operations. The signature tools (`create_signature_request`, `get_signature_request`, etc.) are also clearly separated by lifecycle stage. No two tools appear to do the same thing.

Naming Consistency5/5

Tool names follow a highly consistent `verb_noun` pattern throughout, such as `create_signature_request`, `get_signature_request`, `list_signature_requests`, and `remind_signature`. This pattern is applied uniformly across all major domains (render, signature, template, webhook, trace), making the API predictable and easy for an agent to navigate.

Tool Count4/5

With 59 tools, this is a large surface area, but it is justified by the breadth of functionality: document rendering in multiple formats, e-signatures, template management, webhooks, scheduling, and a crypto/audit trail. While large, each tool has a distinct purpose, and the count feels appropriate for the scope of a comprehensive document automation API. A surface this large risks being overwhelming, but the internal organization is logical.

Completeness5/5

The tool surface is remarkably complete, covering the full lifecycle for multiple domains. For e-signatures, there are tools for CRUD (requests, templates), sending (individual, bulk, envelope), monitoring (get, list), reminders, and certificates. For documents, it covers creation, conversion, editing, merging, splitting, and verification. The inclusion of utility tools like `get_started`, `validate_payload`, and the audit trail tools further solidifies this as a well-considered, production-ready API surface.

Resources