Skip to main content
Glama

Kamy

by dev.kamy

Render PDF asynchronously

render_async

Queue one PDF render and return immediately with { jobId, status: 'queued' } instead of waiting for the document. Choose this over render_pdf when blocking is not acceptable — a heavy template, a large data set, or a turn where you have other work to do — and poll get_job with the returned jobId until status is 'completed' (the finished render, including its download URL, arrives on the job) or 'failed'. Choose render_pdf when a single document is small enough that waiting a few seconds is fine and you want the URL in one call, and render_batch when you have many documents to make at once. Same template, data and page options as render_pdf, and the same one render from the monthly quota — the quota is checked when the job is accepted, so an over-quota call fails here with 402 rather than silently queueing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesData used to populate the template.
nameNoYour own label for this document. Echoed back on the job result and stored on the render, so list_renders can be grepped by it later.
pdf_aNoConvert the output to a PDF/A archival conformance level.
formatNoPaper size. Defaults to a4.
marginNoCSS lengths, e.g. { top: '20mm', bottom: '20mm' }.
metadataNoPDF document properties written into the file's metadata dictionary.
templateYesTemplate slug (e.g. 'invoice') or template UUID. Same values render_pdf accepts.
watermarkNoDraws your own diagonal watermark over every page. Unrelated to the free-plan Kamy watermark, which is applied regardless — see get_account.watermarkPolicy.

TDQS

A4.7/5.0
Behavior5/5

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

Discloses the async return pattern, quota check at acceptance (with 402 error), and that the finished render arrives on the job with download URL. Adds context beyond annotations (readOnlyHint=false, destructiveHint=false) by explaining the polling mechanism and shared options with render_pdf.

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 description is a single paragraph of about 100 words, well-organized: first the core behavior, then usage guidance, then quota and polling. Every sentence adds value, though it could be slightly more compact by combining some ideas.

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?

Given the complexity (async, polling, quota, no output schema), the description covers the return format, polling protocol, quota check, and options shared with render_pdf. It does not provide the full structure of the job result object, but that is partially covered by get_job's description. Overall, it is sufficiently complete for an agent to use 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by noting that name is echoed back, watermark is unrelated to Kamy watermark, and that parameters are the same as render_pdf. This provides agent-level understanding beyond the schema.

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?

Clearly states the verb (queue/return) and resource (PDF render) with the async behavior ('return immediately with { jobId, status: 'queued' }'). Distinguishes from siblings by naming render_pdf and render_batch, making the unique purpose unmistakable.

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 prescribes when to choose this tool ('when blocking is not acceptable'), when to choose alternatives (render_pdf for small documents, render_batch for many documents), and how to use it (poll get_job). Also covers quota behavior and error handling, leaving no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Kamy toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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