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listingbureau

Listing Bureau - Amazon Organic Ranking

lb_projects_get_stats

Read-only

Retrieve daily Amazon organic ranking statistics including SERP positions, ARA analytics, and service execution metrics to track keyword performance and project visibility over time.

Instructions

Get daily stats for a Listing Bureau project: service execution (SFB, ATC, PGV), SERP rankings, ARA analytics, Brand Referral, and Search Query data. Note: this call may be slow (~1-2s) due to 14+ backend DB queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ui_idYesProject unique identifier
daysNoNumber of days of history (default 30, max 365)
Behavior4/5

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

While annotations declare readOnlyHint=true, the description adds critical behavioral context: the specific data domains returned (service execution, SERP, ARA, etc.) and performance characteristics ('14+ backend DB queries', '~1-2s' latency) that help agents understand operational impact. 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?

Two sentences total: first establishes purpose and data scope, second provides performance warning. Information is front-loaded, no redundancy, every clause earns its place regarding functionality or operational characteristics.

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 no output schema exists, the description compensates effectively by listing the 7+ specific data categories returned. The performance disclosure is essential for a potentially slow operation. Minor gap: could mention response format or pagination, but the data type enumeration provides substantial value.

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% (ui_id: 'Project unique identifier', days: 'Number of days of history'), so the schema fully documents parameters. The description provides contextual alignment ('daily stats' implies time-series data matching the days parameter) but does not add syntax or format details 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 uses specific verb 'Get' + resource 'daily stats' and explicitly enumerates the data categories returned (SFB, ATC, PGV, SERP rankings, ARA analytics, Brand Referral, Search Query), clearly distinguishing this from sibling lb_projects_get which likely retrieves project metadata rather than analytics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The performance warning ('may be slow ~1-2s') provides implicit usage guidance about latency expectations, and the enumeration of data types suggests when to use the tool (when those specific metrics are needed). However, it lacks explicit contrast with lb_projects_get or guidance on when NOT to use this versus other project 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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