Zovo Client Report

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

Goes well beyond the annotations: it declares the tool is deterministic ('does not invent numbers or reasons'), that highlights are derived only from computed changes, that rates are recomputed from totals (CTR = clicks / impressions), and that inputs must be supplied rather than fetched. That is substantive behavioral disclosure that readOnlyHint/openWorldHint alone cannot convey.

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 first sentence is front-loaded with triggers and inputs, and the negative-constraint sentence is short and high-value. However the opening sentence is a long run-on enumerating many cases, and the dense prose could be more clearly segmented into discrete sentences.

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?

For a 16-parameter tool with nested objects and all-optional inputs, the description covers when to use it, what inputs are acceptable, what it computes, and what it returns. With an output schema present and 100% parameter coverage, nothing an agent needs in order to call it correctly is missing.

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 already 100%, so 3 is the floor; the description still adds routing value by explaining what forms 'metrics' can take and by naming the concrete export sources that map onto the data/file parameters. It adds context rather than restating the schema, though per-parameter mechanics (date_order, pair_order, paper) are left to 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?

Names a specific verb+resource ('generate a client report') and immediately scopes the accepted inputs (typed metric pairs, pasted table, uploaded CSV/Excel from GA, Search Console, Meta Ads, Shopify) and the artifacts produced (PDF, Excel, CSV). An agent can tell this is a report-builder, not a metrics-analysis utility, without opening the schema.

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?

Strong when-to-use framing ('Use this when the user wants a weekly or monthly client report...') and explicit exclusions ('Does not connect to analytics or ad accounts... is not for forecasts'), which pre-empts a common misroute. It does not name the sibling summarize_metric_changes or state the boundary between the two, so the alternative is left implicit.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds genuine context beyond that: it inherits the input contract of generate_client_report, states it returns absolute and % changes plus rates recomputed from totals and computed highlights, and discloses the two things it will not do (no analytics account access, no causal explanation). Minor overlap with the output schema on returned artifacts keeps it from a 5.

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?

Front-loaded with the usage trigger and example phrasings, then the input contract, then the output artifacts, then the alternative and exclusions — a sensible order. Dense but every sentence carries information; the only slight redundancy is restating returned file formats that the output schema also covers.

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?

For a 10-parameter, nested-object tool with an output schema, the definition covers input modes, the sibling alternative, output artifacts, and explicit scope limits. Nothing needed to call it correctly appears to be missing, though it stays silent on how ambiguous or malformed pasted tables are handled.

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, but the description adds the missing cross-parameter model: the input arrives as typed pairs, a pasted table, or a CSV/Excel export with dates — clarifying that data/file/metrics are alternative entry paths rather than joint requirements (no parameters are required). It does not walk through period, date_order, or pair_order individually, leaving those to 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?

States a specific verb and resource — a period-over-period change table for metrics — and immediately disambiguates from the sibling by naming generate_client_report and the condition that selects it (PDF output). Example user phrasings ('what changed vs last month', 'calculate % change for these KPIs') make the resource concrete.

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?

Explicit when-to-use triggers with quoted user phrasings, an explicit alternative plus its condition (generate_client_report for a client-ready PDF), and explicit when-not boundaries ('Does not connect to analytics accounts and does not explain why numbers changed'). An agent can route between the two siblings without opening either schema.

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