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Q-Radar distribution & gaps

get_qradar_distribution
Read-only

Numeric distribution %: where a quick-commerce platform operates vs where the brand is actually listed, store-type reach (express / instant / longtail), per-state coverage, and a ranked list of gap pincodes with opportunity value (paise). Use for 'where should I expand on Blinkit'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
citiesNoComma-separated city names to filter.
coverageNoFilter the gap list (default all).
platformNoSingle quick-commerce channel code.
storeTypeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety profile is covered. The description adds useful context about the output content (distribution, gaps, opportunity value) and the expansion scoping, which goes beyond the annotations and helps the agent understand what kind of analysis this tool supports.

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 two sentences, front-loads the core output, and includes a practical use case. Every phrase carries meaning with no fluff or repetition.

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?

With an output schema present and good parameter descriptions, the description is reasonably complete. It explains what kind of result to expect (percentages, rankings, pincode gaps) and provides a clear use case. It lacks explicit details on default behavior or parameter combinations, but these are not critical given the schema and output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 75% (three params described in the schema), but the description adds meaning for the storeType param by naming 'express / instant / longtail' in the output context. It also frames the coverage filter and platform in terms of expansion analysis, providing extra semantic value 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's function: it provides numeric distribution percentages, store-type reach, per-state coverage, and a ranked gap pincode list. It distinguishes itself from other Q-Radar tools by emphasizing expansion gaps and opportunity value, and it names a specific use case ('where should I expand on Blinkit').

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

Usage Guidelines4/5

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

The description explicitly gives a usage context ('Use for where should I expand on Blinkit'), which tells the agent when this tool is appropriate. It does not explicitly mention when not to use it or name alternative tools, but the use case is clear enough to differentiate it from siblings.

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

A3.5/5.0
Disambiguation4/5

Most tools target distinct data points (e.g., get_analytics_summary vs get_brand_score). However, 'search' and 'search_catalog' have overlapping functionality and could cause confusion, and 'fetch' is a helper tied to 'search', adding minor ambiguity.

Naming Consistency4/5

The majority use consistent verb_noun snake_case (e.g., get_inventory, list_stores). Exceptions like 'fetch' and 'search' (without object) break the pattern, but they are few.

Tool Count2/5

43 tools is excessive for a data-retrieval-only API. Many get_* and search_* tools could be consolidated (e.g., search_catalog, search_inventory, search_orders are similar). The large number will overwhelm an agent.

Completeness2/5

The tool set is entirely read-only (get, search, list, fetch). There are no create, update, or delete tools, which is a critical gap for managing e-commerce operations. Agents can only view data, not act on it.

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