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

Substantial behavior is disclosed beyond the annotations: deterministic computation, zero LLM and zero network, only the bundle's sha256 retained, an explicit 4 MB per-request cap, and an output delivered as a Markdown `output` beside a `metrics` JSON. The attestation caveat ('proves that input produced this output, not that the input is complete') is unusually honest context an agent could not infer from annotations. Minor tension: 'zero network' and 'computed deterministically' sit awkwardly against openWorldHint=true and idempotentHint=false, though the per-call x402 payment plausibly explains the non-idempotency.

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 definition is a single wall of prose that opens with a price tag rather than the operation, then interleaves pricing, a URL, a 64-character sha256, an attestation disclaimer and upsell language. Several facts (the $25 tier, the 4 MB cap, the extractor fallback) are each stated in more than one place. Useful content exists but is not front-loaded or partitioned.

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?

There is no output schema, and the description does compensate by naming the deliverable (`output` Markdown plus `metrics` JSON). However, for a tool whose entire premise is ingesting a user's transcript bundle, the description never explains how that bundle reaches the call, and the only schema field (`task`) is inconsistent with the inputs the description talks about. That gap is fatal to correct invocation despite the otherwise rich behavioral detail.

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?

The schema exposes exactly one parameter, `task`, described only as 'Task/query text for this SKU', and the description never mentions `task` at all. Instead it discusses `records`, `summary`, `git_log`, `decisions` and `instructions`, none of which exist in the input schema — so the description actively points the agent at an input surface it cannot use. The critical question of how to actually supply the transcript bundle is left unresolved.

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 names a concrete computed resource — 'agent:human hours (40 h/week parity + measured engaged) and human-wait share with the > 30 min wait exclusion' — over 'YOUR OWN Claude Code transcript bundle', and explicitly positions itself as the '$0.25 entry rung' below 'ops.agent-workforce-report ($25)'. An agent can distinguish it from the sibling report without opening either schema. It falls short of 5 only because the core verb is buried under pricing and marketing framing in a single dense block.

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?

It gives explicit routing: upgrade to ops.agent-workforce-report ($25) for reading load, meta-work share, skill use and full parity notes, and use the free local extractor when transcripts exceed the 4 MB cap or the user does not want to send them. It also states which optional inputs (git_log / decisions / instructions) are ignored at this tier, preventing misfires. It does not state any exclusions beyond the size/privacy boundaries.

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