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Kamy

by dev.kamy

List schedules

list_schedules
Read-only

List this account's recurring render schedules, newest first, with the id needed to delete one. Use it to answer what is already automated before creating a duplicate, and to diagnose a schedule that is not producing documents: each row carries enabled, schedule, timezone, next_run_at, and last_run_at / last_run_status / last_run_error from the most recent firing — last_run_error is where a delivery or quota failure shows up. Returns { schedules, total, limit, offset }. Read-only and spends no quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return, 1-100. Defaults to 50.
offsetNoRows to skip. Defaults to 0.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, confirming no side effects. The description adds beyond that: it explicitly states 'Read-only and spends no quota', which adds useful behavioral context (quota consumption) beyond the annotations. It does not contradict 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?

The description is concise with no wasted words. It front-loads key info: what it lists, ordering, and primary use cases. Every sentence adds value, covering purpose, use cases, fields, return structure, and safety. Ideal length for a simple list tool.

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 simple schema (2 optional params), no output schema, and strong annotations, the description covers purpose, return fields, usage scenarios, and safety comprehensively. The only gap is that it doesn't list all response fields explicitly, but the sample ('schedules, total, limit, offset') covers the structure well.

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 explicit descriptions for 'limit' and 'offset' (defaults, min/max). The description adds high-level context about pagination (total, limit, offset in response) but no param-level details beyond what's in the schema. Baseline 3 is appropriate.

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 specifies that the tool lists recurring render schedules with details like 'enabled, schedule, timezone, next_run_at', and explicitly distinguishes its purpose from sibling tools like 'create_schedule' and 'delete_schedule'. The verb 'list schedules' combined with concrete fields makes the purpose clear and specific.

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?

The description explicitly tells when to use this tool: 'to answer what is already automated before creating a duplicate' (avoiding duplicate creation) and 'to diagnose a schedule that is not producing documents' (troubleshooting). This provides clear context for choosing it over create_schedule, delete_schedule, or other tools.

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