validate_payloadDry-run a render payload against a template's schema WITHOUT producing a PDF or using quota. Returns per-field self-healing errors (expected type, value received, allowed values, an example) so you can fix the data before render_pdf. Requires a Kamy API key.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The data payload to validate against the template schema. | |
| template | Yes | Template slug (e.g. 'invoice') or template UUID. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly, non-destructive), the description adds valuable behavioral details: it does not generate a PDF or use quota, requires a Kamy API key, and returns self-healing per-field errors including expected type, value received, allowed values, and an example. This exceeds the annotation baseline and provides significant operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loads the primary action, and every clause adds value—mentioning the dry-run nature, the return error format, the parallel to render_pdf, and the auth requirement. Nothing is redundant or wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, the description covers the purpose, usage timing, return behavior, and auth requirement. Since there is no output schema, the description sufficiently explains what the tool returns (self-healing errors). It leaves minimal gaps for an agent to misuse the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters already described ('data' as the payload and 'template' as slug or UUID). The description does not add extra meaning beyond the schema, so it stays at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates a payload against a template schema, with a specific verb ('Dry-run'), and distinguishes itself from render_pdf by explicitly noting it does NOT produce a PDF or consume quota. This makes the purpose unambiguous and differentiates it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context by saying it lets you 'fix the data before render_pdf,' implying it should be used prior to rendering. However, it does not explicitly mention when not to use it or name alternative tools like get_template_schema, so it falls short of a full 5.
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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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.
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.
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.
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.