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Kamy

by dev.kamy

Record a batch of LLM calls (Kamy Trace)

trace_record_batch

Write up to 100 Kamy Trace records in one call. Reach for this over trace_record whenever you have more than a couple of buffered LLM calls to persist — it is one auth, one quota check and one round trip instead of N. Each element takes exactly the shape trace_record takes. The whole batch is counted against the monthly Trace quota up front, so a batch that would cross the plan cap is rejected in full with 402 and nothing is stored; split it or upgrade rather than retrying. Returns { records: [{ id, content_sha256, signature, recorded_at, verify_url }] } in input order. Requires a Kamy API key with the trace:record scope; without a key, returns dashboard setup instructions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYes1–100 records, each exactly the shape trace_record takes.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (which are minimal), the description discloses key behaviors: the batch is counted against quota upfront, a rejected batch returns 402 and stores nothing, and the return format is provided. No contradictions 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?

The description is concise (about 5 sentences) and front-loaded with purpose, then usage, then behavioral details, then return format, then auth. Every sentence adds value with no redundancy.

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?

Given the tool's complexity (batch operation, quota, auth, return format), the description covers all essential aspects: capacity, when to use, quota behavior, identical shape to trace_record, return structure, and auth requirements. No output schema exists, but the return format is explicitly described.

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 coverage is 100%, so the baseline is 3. The description adds that each element takes the same shape as trace_record, which is helpful but not necessary since the schema fully defines each field. No additional parameter semantics 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?

The description clearly states the tool writes up to 100 Kamy Trace records in one call, with a specific verb ('Write') and resource. It explicitly distinguishes from the sibling tool trace_record by recommending this tool when there are more than a couple of buffered calls, which sets it apart effectively.

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?

Provides explicit guidance on when to use this tool over trace_record ('more than a couple of buffered LLM calls'), explains the quota rejection behavior and how to handle it ('split it or upgrade'), and mentions the required API key scope. This gives the agent clear decision-making criteria.

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