harvest_client_intake

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

With no annotations provided, the description carries the full burden. It discloses a meaningful behavioral constraint (no placeholders), which is useful, but it doesn't explain what happens with missing/invalid data, side effects, or the return behavior. The anti-placeholder rule adds transparency, but significant gaps remain.

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?

Two short sentences with no filler. The main purpose is front-loaded, and the behavioral rule is added succinctly. Every word earns its place; no redundancy or extraneous detail.

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?

This is a 10-parameter tool with no output schema and no annotations. The description gives a high-level purpose but fails to explain what the tool returns, any required parameter combinations, or how the parameters interrelate. An agent must infer a lot about expected inputs and outcomes, making it incomplete for a complex intake 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 coverage is 100%, so the baseline is 3. The description adds a high-level mapping to the parameters (experience, assets, metrics) and reinforces the 'real data' requirement, but it doesn't provide per-parameter syntax, examples, or relationships beyond the schema. It provides some context but doesn't compensate for any gaps (and there are none in the schema).

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 a verb ('extracts') and resource ('real practitioner experience, assets, and metrics'), and adds a distinct behavioral rule ('Bans AI from inventing default placeholders') that differentiates it from a generic data collector. However, it doesn't explicitly distinguish from sibling tools, though they are clearly unrelated in domain.

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 implies a use case (gathering real practitioner data) but provides no explicit guidance on when to use this tool versus alternatives or any exclusions. The sibling tools are obviously different (validation, matrix building, etc.), so the ambiguity is low, but no direct usage direction is given.

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