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Kamy

by dev.kamy

Get render

get_render
Read-only

Fetch one previously produced document by its render id, with a freshly signed download URL valid for the next hour. This is the recovery path for an expired link: the URL is minted at read time, so calling this again on an old render always yields a working download. Use list_renders first if you do not have the id. Returns { id, name, templateId, templateName, status, bytes, durationMs, url, createdAt }, where url is null when the render failed, has not finished yet, or its file has aged out of retention — check status before assuming a download exists. Read-only and spends no quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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?

Annotated as readOnlyHint and destructiveHint false, the description adds significant behavioral detail: the URL is freshly signed and valid for 1 hour, calling again always yields a working download, URL is null under specific failure/retention conditions, and it spends no quota. No contradiction with annotations.

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?

Four sentences, no redundancy. The main action and immediate benefit (fresh URL) are first, followed by usage guidance, return fields, and read-only note. Every sentence adds value.

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?

For a simple fetch tool with one parameter and no output schema, the description fully explains the return structure, edge cases (null URL), idempotency, and quota impact. No missing information needed for an agent to use this correctly.

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?

The input schema already describes render_id as a UUID with constraints. The description adds context by listing the source tools (render_pdf, render_async/get_job, list_renders) that produce the id, which helps the agent construct valid parameters. This is beyond the schema description.

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 the specific verb 'Fetch' and identifies the resource as 'previously produced document by its render id'. It distinguishes from siblings by advising to use list_renders first if the id is unknown, and implicitly differentiates from render_pdf (which produces) and get_render_pages (pages).

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?

Explicitly states when to use (recovery path for expired links, when you have the render id) and provides an alternative (list_renders if id not known). It does not explicitly exclude other scenarios like fetching pages, but the context is clear enough for an AI agent.

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