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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that the tool returns document text for deep research, giving insight into the output beyond annotations.

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 sentences with no redundant information. The purpose and key detail are front-loaded.

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?

The description covers what the tool does, how to use it, and what it returns. With an output schema present, it is sufficiently complete for this simple fetch operation.

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 sole parameter 'id' is fully described in the schema (100% coverage). The tool description reiterates that it comes from search but adds no new parameter details beyond 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 specifies the verb 'fetch', the resource 'full detail of a result', and explicitly ties it to 'search' results by id. This clearly distinguishes it from sibling tools like 'search' and other get_* tools.

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 states to use this tool after obtaining an id from 'search', providing clear context. While it doesn't state when not to use, the guidance is sufficient for typical use cases.

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 and destructiveHint=false, so the safety profile is clear. The description adds context on the data contents (user details and corporate profile), but does not disclose additional traits like authentication requirements or rate limits.

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 description is a single sentence that efficiently conveys the purpose and contents. No extraneous words; front-loaded with key information.

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 simple read-only tool with no parameters and an output schema, the description adequately specifies the returned data. It covers the essential elements needed for an agent to understand what to expect.

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?

Input schema has 0 parameters and 100% coverage, so no param info is needed. Baseline for 0 params is 4. The description does not add parameter info, which is acceptable.

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 the tool retrieves the workspace profile, listing specific elements like user details and corporate/tenant profile (legal name, tax IDs, etc.). It avoids tautology and distinguishes from siblings by focusing on account/profile data.

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 provides no guidance on when to use this tool versus siblings (e.g., get_brand_pack, get_analytics_summary). No 'when to use' or 'when not to use' information is given.

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 and destructiveHint=false. The description adds concrete behavioral details (auto-recharge status, last top-up) beyond annotations, providing useful context without contradiction.

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?

Single, front-loaded sentence that efficiently conveys the tool's purpose and outputs. No wasted words.

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?

Given the tool's simplicity (no parameters, read-only, output schema exists), the description fully covers the context. It lists all key fields implied by the output schema.

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?

No parameters exist, so schema coverage is 100%. The description does not need to add parameter information; baseline 4 is appropriate as the description adds value by listing return fields.

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 the tool retrieves AI wallet information and lists specific fields (balance, month-to-date spend, monthly cap, auto-recharge status, last top-up). It distinguishes from sibling tools by focusing on wallet data.

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 usage for checking wallet details but provides no explicit guidance on when to use or not use this tool vs alternatives. No exclusions or context about prerequisites.

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, so the tool is clearly read-only. The description adds context about the data (sessions, users, etc.) but does not disclose additional behavioral traits like query limits or data freshness. Since annotations cover the safety profile, a score of 3 is appropriate.

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 description is a single sentence that is concise and front-loaded with the essential information. Every word contributes meaning without redundancy.

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?

Given the presence of an output schema (not shown but indicated), the description does not need to explain return values. It lists the included metrics (sessions, users, conversions, etc.) which is sufficient for understanding the tool's output. The tool is simple with one parameter, and the description is complete.

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 description coverage is 100% for the single parameter 'range', which is fully described in the input schema with enum values. The description does not add semantics beyond the schema; it only lists the metrics returned. Baseline 3 is correct.

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 identifies the tool as a Google Analytics 4 summary for a specified time window, listing specific metrics (sessions, users, conversions, etc.). This distinguishes it from sibling tools like 'get_marketing_summary' by focusing on GA4 and detailed metrics.

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 usage for retrieving a GA4 summary over a window, but does not explicitly state when to use this tool versus alternatives such as 'get_marketing_summary'. No when-not or alternative tool guidance is provided.

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 and destructiveHint=false. The description adds context by listing the specific attributes returned (logo, colors, etc.), but does not disclose any additional behavioral traits beyond those from annotations.

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 description is a single sentence, concise and front-loaded with the key term 'Brand identity configuration'. It efficiently lists the contained attributes.

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?

Given no parameters and the existence of an output schema, the description adequately outlines the contents of the brand pack. It does not need to explain return values per rules.

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?

There are no parameters, so the description adds meaning by enumerating the fields that constitute the brand identity configuration. Schema coverage is 100% trivially.

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 that the tool returns brand identity configuration including specific elements like logo, colors, fonts, etc. It distinguishes from sibling tools like get_brand_score by focusing on identity configuration rather than scoring.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or when not to use it.

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 read-only and non-destructive behavior. The description adds that it returns the most recent day's score and the listing count, which provides useful context beyond annotations but does not disclose additional behavioral traits like rate limits or data freshness.

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 sentences, no fluff. All information is pertinent and front-loaded.

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?

Given no parameters, clear annotations, and an existing output schema, the description fully explains what the tool does and what it returns. No gaps remain.

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?

There are no parameters, and the schema coverage is 100% (empty). The description does not need to explain parameters, and with zero parameters, a baseline of 4 is appropriate.

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 precisely states that the tool returns the latest brand-health score, explaining it as a weighted average across tracked marketplace listings. This clearly identifies the resource and purpose, distinguishing it from sibling tools like get_analytics_summary or get_dashboard_summary.

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 usage when the latest brand-health score is needed. While it does not explicitly state when not to use it or list alternatives, the context of it being a focused, zero-parameter tool makes its application clear.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds that it returns order counts per channel, which is consistent and provides additional behavioral context without contradicting annotations.

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 description is two sentences long, no redundant words, and front-loads the key information. Every sentence adds value.

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 simple tool with no parameters and an output schema (presumably rich), the description adequately explains what is returned and its use. No gaps remain.

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 input schema has zero parameters and 100% coverage. According to guidelines, the baseline is 4 for zero parameters. The description does not need to add parameter semantics, and it appropriately focuses on the tool's purpose.

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 it gets order volume across non-Amazon marketplaces, listing specific channels (Snapdeal, Flipkart, Myntra, JioMart, Meesho) and indicating it returns synced order counts. This distinguishes it from siblings like get_orders_summary or get_order.

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 provides context on when to use it ('see which marketplaces have data and their relative scale'), but does not explicitly mention when not to use it or suggest alternatives. This is sufficient for a tool with no parameters and clear purpose.

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 and destructiveHint=false. The description adds context about output content (counts, breakdown, recent violations) but no additional behavioral traits beyond annotations.

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?

Single sentence with no waste, front-loaded with key terms. Efficient and to the point.

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?

Sufficient for a read-only, no-parameter tool with output schema present. The description covers the essential return values. Could be slightly more detailed on usage context.

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?

Zero parameters with 100% schema coverage, so baseline is 4. The description adds meaning about the output structure, which compensates for the lack of parameter details.

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 specifies the tool returns MAP/MRP compliance violations with open and escalated counts, breakdown by channel, and most recent open violations. It uses specific verbs and resources, differentiating it from sibling tools.

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?

No explicit guidance on when to use this tool versus alternatives like get_marketplace_alerts or get_notifications. The description implies usage for compliance checks but lacks exclusions or context.

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 and destructiveHint=false, so the description does not need to re-state safety. It adds context about the output (integrations status, recent activity) but no additional behavioral traits beyond those provided by annotations.

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 description is extremely concise at two sentences, front-loading the purpose and immediately providing usage guidance. Every sentence adds value with no wasted words.

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?

Given zero parameters and the presence of an output schema, the description adequately explains what the tool returns: high-level health, integration status, and recent activity. It covers the necessary information for an overview tool without needing to detail return values.

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 tool has zero parameters, and schema coverage is 100%. The description does not need to explain parameters. It provides context about what the output covers, which is sufficient.

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 it provides high-level health of the workspace, specifically connected integrations by status and recent account activity. It also gives a concrete question it answers ('how is my business set up / what's connected'), distinguishing it from other more specific get_* tools.

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 explicitly says 'Use this first', indicating it should be the initial tool for overview questions. It implies that for specific details, other tools are more appropriate, though it does not list exclusions or alternatives explicitly.

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, so the description adds value by detailing output content (aging buckets, fees, actions). It does not contradict annotations and no side effects are expected.

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?

A single sentence packs all key information concisely. No filler or redundancy.

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?

Given no parameters and an output schema present (inferred), the description adequately explains what the tool returns. For a simple read-only health report, it is fully informative.

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?

There are zero parameters, so the baseline is 4. The description does not need to explain parameters but effectively describes the tool's output.

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 the tool is for Amazon FBA inventory health, listing specific outputs (aging buckets, storage-fee exposure, recommended actions). It distinguishes from siblings like get_inventory by focusing on health metrics, not just stock levels.

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 the tool is for assessing FBA health but does not explicitly state when to use it over alternatives like get_inventory or search_inventory. No when-not guidance is provided.

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 and destructiveHint=false, so the tool is clearly safe. The description adds context about the specific sections (B2B, B2CL, B2CS) and HSN rollup, which goes beyond annotations. No contradictions.

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?

A single sentence front-loads the core purpose (GSTR-1 outward-supply summary) and includes essential details (sections, rollup, purpose). No unnecessary words or repetition.

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?

Given the tool has a single optional parameter, an output schema exists, and annotations cover safety, the description provides enough context: it describes the output content (section totals, HSN rollup) and purpose. No gaps.

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 only parameter 'period' has full schema coverage (100%) describing the enum values. The description mentions 'for a period' but does not add syntax or format details beyond the schema. Baseline of 3 is appropriate since schema does the heavy lifting.

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 the tool returns a GSTR-1 outward-supply summary for a period, specifying B2B/B2CL/B2CS section totals and HSN-wise rollup. It explicitly mentions the purpose (return preparation) and the resource (GSTR-1), distinguishing it from sibling tools like get_tax_summary.

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 usage for return preparation, but does not explicitly state when to use this tool over alternatives (e.g., get_tax_summary, get_state_wise_tax). No when-not or exclusion guidance is provided, leaving the agent to infer context from the tool name and title.

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 and destructiveHint=false. The description adds that it is a rollup for a period, but no additional behavioral context like rate limits or auth needs. Adequate but not extra.

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?

Single sentence, front-loaded with purpose and details. No wasted words.

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?

Given one optional parameter with full schema documentation, annotations, and an output schema (presence known), the description is complete for an agent to understand its role.

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% and includes a description for the single parameter 'period'. The tool description adds no extra meaning beyond 'for a period'. Baseline 3 applies.

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 it provides a per-HSN-code tax rollup with specific components (taxable value, CGST/SGST/IGST/cess, invoice count) for a period, differentiating it from sibling tools like get_tax_summary.

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?

No guidance on when to use this tool versus siblings (e.g., get_tax_summary, get_gstr1_summary, get_state_wise_tax). The agent has to infer usage from the purpose alone.

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 and destructiveHint=false. The description adds that the tool returns joined data (available, reserved, etc.) and enforces a max row limit, providing transparency beyond annotations.

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?

Single sentence fully covers purpose, features, and constraints without redundancy. Every word adds value.

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 simple read tool with output schema and annotations, the description provides all necessary details: data returned, optional search, and pagination limit. Complete and sufficient.

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 parameters are well-defined. The description adds context by stating query is free-text SKU search and results are limited to 25 rows, enhancing understanding beyond the schema alone.

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 it retrieves warehouse stock levels joined with master product data, specifying exact fields (SKU, available, reserved, incoming, blocked, name, cost, MRP). It distinguishes from siblings like search_inventory by emphasizing the join and structured output.

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?

Provides guidance on optional free-text SKU search and row limit (25). While it doesn't explicitly contrast with alternatives, the description implies it's for batch queries with a basic search, which is adequate for a simple tool.

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 indicate read-only and non-destructive behavior. The description adds value by noting the PDF link is short-lived, providing temporal context beyond annotations.

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?

Single sentence of 11 words, front-loading the essential components. Every word is meaningful and no redundancy.

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?

Given the output schema exists and only one required parameter, the description sufficiently covers what the tool returns. Could mention it returns a single object, but not necessary.

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%, and the description does not add meaning beyond the schema's parameter description. Baseline score is appropriate.

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 it retrieves a manifest detail containing shipment info, order IDs, state, and a short-lived PDF link. This is specific and distinguishes it from siblings like list_manifests.

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 usage for getting a specific manifest's details, but lacks explicit guidance on when to use vs alternatives (e.g., list_manifests) or any exclusions.

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 and destructiveHint=false. The description adds the behavioral detail that it returns up to 25 rows, which is useful but not extensive. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, no wasted words. It front-loads the purpose and adds the row limit efficiently.

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 simple read-only tool with an output schema, the description covers the returned data (margin config per product, COGS, platform-fee, optional per channel) and the row limit. No critical missing context.

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%, with the single 'limit' parameter fully described. The description's mention of 'up to 25 rows' aligns with the schema but adds no additional meaning beyond the parameter's max constraint.

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 it retrieves per-product margin configuration for P&L, specifying COGS and platform-fee percentage, optionally per channel. It distinguishes from the many sibling 'get_*' tools by focusing on margin configuration.

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 use for P&L analysis but does not provide explicit guidance on when to use this tool versus alternatives like get_pnl_summary. No when-not-to-use or exclusion criteria are mentioned.

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 declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds value by specifying the exact metrics returned (CTR, CPC, ROAS, etc.) and the inclusion of prior-period deltas and platform breakdowns, which are behavioral details beyond mere read-only status.

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 description is a single, well-structured sentence that efficiently conveys all key aspects: cross-platform scope, window, metrics list, deltas, and platform breakdown. No unnecessary words, and critical information is front-loaded.

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?

Given the tool's moderate complexity (multiple platforms, metrics, deltas) and the presence of an output schema (not shown but indicated), the description covers the main return value content. However, it does not mention prerequisites (e.g., required permissions) or whether the summary includes all ad platforms or only those with data. Slightly complete but with minor gaps.

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 input schema has 100% description coverage for the single parameter 'range', explaining it as the reporting window with enum values and a default. The description adds little beyond that, only mentioning the window generically. Baseline score of 3 is appropriate since the schema already conveys the parameter semantics well.

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 the tool provides cross-platform ad performance metrics (spend, impressions, etc.) for a specified window, with prior-period deltas and per-platform breakdown. It is specific about the resource (marketing summary) and differentiates from sibling tools like get_analytics_summary or get_orders_summary which cover different domains.

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 the tool is for retrieving marketing ad performance data, but does not explicitly state when to use it versus alternatives like get_analytics_summary. No exclusion criteria or alternative suggestions are provided, leaving the agent to infer usage context from the purpose alone.

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 and destructiveHint=false, so the description adds context about returning counts and recent events. However, it does not disclose any additional behavioral traits such as data freshness, pagination, or limitations beyond the one parameter.

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 description is a single, well-structured sentence that efficiently conveys the tool's purpose, input window, and output (counts by type/severity and recent events). No unnecessary words or repetition.

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?

Given the simple parameter set (1 optional), strong annotations, and existence of an output schema, the description covers the core functionality. It could benefit from briefly noting what the counts represent or that it summarizes alerts, but it is largely complete for an agent.

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 description coverage is 100% for the single parameter 'days', fully described as 'Lookback window in days (default 7).' The description's phrase 'fired in a window' adds minimal new meaning beyond the schema. With high coverage, baseline 3 is appropriate.

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 the tool retrieves marketplace alert events (price drops, OOS, MAP breaches) within a time window, providing counts by type and severity and the most recent events. This specific verb+resource distinguishes it from sibling tools like get_notifications and get_analytics_summary.

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 usage for monitoring marketplace alerts (price drops, OOS, etc.) but does not explicitly state when to use this tool versus alternatives like get_notifications or get_analytics_summary. No exclusions or prerequisites are mentioned.

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 and destructiveHint=false. The description adds the specific fields returned but does not disclose ordering, pagination, or other behavioral details. With annotations covering safety, the description provides adequate but not extra context.

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?

Single sentence, no filler, front-loads key information. Every word adds value.

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?

Given no parameters, good annotations, and existence of output schema, the description is sufficient. It could specify ordering (e.g., chronological) but not essential.

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?

No parameters exist, so baseline is 4 per instructions. The description adds no parameter-specific meaning but is not required.

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 the tool returns in-app notifications with specific fields (severity, title, state). It distinguishes itself from numerous sibling 'get_*' tools by focusing on notifications.

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 usage for retrieving recent notifications, but does not explicitly exclude alternatives or provide guidance on when not to use it. However, given the clear purpose, an agent can infer when to select this tool.

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 and destructiveHint=false. The description adds value by detailing what the response includes (line items, state transitions, etc.). No contradictions. For a read operation, this is sufficient behavioral disclosure.

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 sentences with zero wasted words. First sentence lists tool output, second sentence directs to the sibling for input. Information is front-loaded and scannable.

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?

Given the tool's simplicity, one required parameter, and output schema presence, the description fully covers what the agent needs to know. No missing details about return values or behavior.

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?

Input schema covers the single parameter with a description and regex pattern (100% coverage). The description adds context by explaining that the id originates from search_oms_orders, which helps the agent understand how to source the value.

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 the tool retrieves full detail for a single fulfillment order, listing specific content fields (line items, lifecycle history, credit consumption, etc.). It distinguishes from sibling tools like search_oms_orders by specifying the purpose of getting one order's details.

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?

Explicitly tells the agent to obtain the id from search_oms_orders, indicating a clear prerequisite and a sister tool. No explicit 'when not to use' guidance, but the context makes the usage scenario unambiguous.

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 and destructiveHint=false. The description adds valuable behavioral context: money in minor units (paise for INR), inclusion of linked returns and channel-specific fields, enhancing transparency beyond annotations.

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 sentences, no wasted words. Front-loaded with purpose and detail, followed by a critical usage hint. Every sentence adds value.

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?

Given the presence of an output schema and annotations, the description covers the essential aspects: full detail, linked return, channel-specific fields, minor units. It is complete for an agent to select and invoke the tool correctly.

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?

With 100% schema coverage, baseline is 3. The description adds practical semantics by telling agents to get the channel and lineItemId from search_orders, which aids correct parameter usage.

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 the verb 'get' and the resource 'order detail' with specific identification via channel and lineItemId. It distinguishes itself from sibling tools like search_orders and get_orders_summary by specifying it returns full detail for a single order.

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 advises to obtain the channel and lineItemId from search_orders, providing clear context for when to use this tool. While it doesn't list all exclusions, it effectively guides the agent on prerequisite steps.

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 and destructiveHint=false, so the tool is safe. The description adds that output is aggregated (volume per channel), which is consistent but already implied by the name. No additional behavioral traits (e.g., rate limits, data freshness) are disclosed.

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 description is concise at two sentences, with the core purpose front-loaded. It efficiently communicates what the tool does and its intended use, without unnecessary elaboration.

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?

Given the tool has one optional parameter, clear annotations, and an output schema (indicated), the description adequately covers purpose and usage. It does not need to detail output format, as the schema handles that. Some might want default behavior noted, but the schema already says default 30.

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 description coverage is 100% for the single parameter 'days', which already has a clear description and constraints. The tool's description mentions 'over a window' but does not add new semantic detail beyond 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 the tool returns 'Order volume per channel over a window, across all connected marketplaces', specifying the aggregation level and scope. It also explains its use case: seeing where orders come from and relative scale per channel, distinguishing it from siblings like 'get_order' or 'get_channel_orders_overview'.

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 advises using it 'to see where orders are coming from and the relative scale per channel', providing a clear use case. However, it does not specify when not to use this tool or explicitly compare it to alternatives like 'get_channel_orders_overview', leaving some ambiguity.

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 indicate safe read-only behavior (readOnlyHint=true, destructiveHint=false). The description adds that it returns net amount and count per channel, but does not elaborate on pagination, rate limits, or other behaviors. It provides modest additional context.

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?

A single, efficient sentence that front-loads the core purpose. Every word adds value with no redundancy or filler.

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?

Given the simple input schema (one optional parameter) and presence of an output schema, the description adequately covers the tool's return values. It might benefit from mentioning the default window or that it aggregates across channels, but overall sufficient.

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 single parameter 'days' is fully described in the schema (100% coverage). The tool description mentions 'over the window' but does not add significant meaning beyond the schema's description of the lookback window and default.

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 the tool returns settlement/payment activity: the net amount settled plus count of records per channel. This distinguishes it from sibling tools like get_orders_summary or get_pnl_summary, which cover different financial data.

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?

No explicit guidance on when to use this tool over alternatives. It does not mention when-not to use it or provide comparisons with other summary tools in the sibling list.

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 indicate read-only and non-destructive. The description adds that the pick-PDF link is short-lived, a behavioral detail beyond what annotations provide. No contradictions.

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?

One concise sentence that conveys the key purpose and outputs. No unnecessary words.

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?

With an output schema present, the description covers the essential information. For a single-parameter retrieval tool with excellent schema coverage and annotations, it is complete.

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 baseline 3. The description does not add information about the 'id' parameter beyond what the schema provides. The listed fields are output, not parameter details.

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 it retrieves details of a single picklist, specifying the fields returned. It distinguishes from siblings like 'list_picklists' (which lists multiple) and other 'get' tools for different entities.

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 name and description imply it's for fetching a specific picklist by ID. However, it lacks explicit guidance on when not to use it or alternatives, though the sibling list provides context.

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 and destructiveHint=false, indicating a safe read operation. The description adds value by noting that amounts are in major currency units, but does not disclose any other behavioral traits (e.g., pagination, rate limits). The bar is lowered due to annotations; the description provides marginal additional context.

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 description is concise (two sentences) and front-loads the key components. The list of metrics is dense but necessary; no extraneous information. It is appropriately sized for the tool's complexity.

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?

Given the presence of an output schema and rich annotations, the description sufficiently covers the tool's context. It mentions the settlement-based nature, the window parameter (implied), and key items returned. It does not explain TCS/TDS but is otherwise complete for a single-parameter financial 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?

The input schema already fully describes the 'days' parameter (type, range, default), with 100% coverage. The tool description does not add any additional semantics beyond the schema, so it meets the baseline but does not exceed it.

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 it provides an 'Amazon settlement-based money summary' and lists specific financial metrics (revenue, fees, refunds, etc.). It effectively conveys the tool's purpose and resource, though it does not explicitly differentiate from siblings like get_payments_summary or get_reimbursements_summary.

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?

No guidance on when to use this tool versus alternatives (e.g., get_payments_summary). The description only explains what it does, without indicating when it is appropriate or inappropriate. There are no explicit usage conditions or exclusions.

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 indicate readOnlyHint=true, destructiveHint=false. The description adds the subscription requirement and clarifies it returns top movers over a window. No additional behavioral traits (e.g., rate limits, permission details) are disclosed, but annotations cover safety.

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 description is a single sentence that front-loads key information, defines the acronym PSL, and avoids extraneous words. Every element earns its place.

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?

The tool has an output schema, so return values need not be described. The description covers the window, channel, and subscription requirement. It is complete for a simple tool with two well-documented parameters and no complex behavioral aspects.

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?

Both parameters (days, limit) have descriptions in the input schema (100% coverage), so the description adds no further semantic value beyond what the schema already provides. Baseline 3 is appropriate.

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 the tool returns 'Top potential-sales-loss (PSL) movers' from quick-commerce shelf intelligence, specifying the metric (estimated sales loss to out-of-stock) and grouping by channel. It distinguishes itself from sibling tools as no other mentions PSL or this specific combination.

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 mentions the prerequisite 'Requires a Q-Radar subscription' and implies usage when needing PSL mover data. While it doesn't explicitly state when not to use or list alternatives, the niche purpose makes its context clear.

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, so the tool's safety profile is known. The description adds value by detailing the output (distribution %, gap pincodes with opportunity value), which goes beyond the structured annotations.

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 sentences with no wasted words: the first lists what the tool provides, the second gives a clear use case. Front-loaded with essential information, making it easy for an agent to quickly understand the tool's purpose.

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?

Given the presence of an output schema and annotations, the description adequately explains the tool's output (distribution %, gap pincodes). It could mention default behavior when no parameters are provided, but overall it is sufficiently complete for activation.

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 description coverage is 75%, so most parameters are already documented. The description adds context for the storeType parameter by listing the enum values ('express / instant / longtail'), compensating for its missing schema description.

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 specifies that the tool provides numeric distribution percentages, comparisons of platform operation vs brand listing, store-type reach, per-state coverage, and a ranked list of gap pincodes with opportunity value. It explicitly states the use case ('where should I expand on Blinkit'), distinguishing it from sibling tools like get_qradar_overview or get_qradar_sov.

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 includes an explicit use case ('Use for 'where should I expand on Blinkit''), giving clear context. It does not mention when not to use the tool or list alternatives, but the purpose is clear enough for an agent to differentiate from siblings.

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 and destructiveHint=false, so the description aligns. It adds behavioral context by detailing the contents of the ledger (out-of-stock windows, lost units/revenue, audit hash chain) and ranking behavior, which goes beyond annotations.

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 sentences, no superfluous words. The first sentence immediately states the tool's purpose and value proposition, and the second enumerates contents. Highly efficient.

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?

Given the tool has 4 optional parameters, 100% schema coverage, and an output schema, the description covers the essential behavioral aspects (contents, ranking). It does not explain return structure, but the output schema fills that gap. Overall, it is sufficient for an agent to understand the tool's value.

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%, and each parameter has a description. The description adds high-level context about the data but does not provide additional meaning for individual parameters beyond what the schema already offers. Baseline of 3 is appropriate.

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 the tool retrieves a 'potential-sales-loss ledger' with specific details like lost units, lost revenue, velocity basis, and audit hash chain. It distinguishes from sibling tools by specifying the CFO-grade financial focus and ranking by impact.

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 usage for high-level financial analysis ('CFO-grade') but does not explicitly state when to use this tool versus alternatives like get_psl_movers or get_qradar_overview. No when-not guidance is provided.

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 and destructiveHint=false, so the description adds value by noting that PSL is in paise and should be converted to INR, but overall it does not disclose additional behavioral traits beyond what annotations provide.

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 description is two sentences with no wasted words. It front-loads the key outputs and ends with a concrete usage example. Every sentence earns its place.

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?

Given the tool has an output schema, the description covers the main KPIs sufficiently. It could mention more about parameter usage, but for a quick overview with no required parameters, it is largely complete.

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 each parameter is already described in the schema. The description adds no new parameter information; it only references pincode coverage in the output. Baseline score of 3 is appropriate.

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 the tool returns quick-commerce shelf-intelligence KPIs like OSA, PSL, etc., with a specific verb 'get' and resource 'overview'. However, it does not explicitly differentiate from similar sibling tools like get_shelf_availability or get_qradar_sov, so it loses a point.

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 provides a clear use case ('Use for 'how is my quick-commerce availability'), implying when to use, but it does not mention when not to use or suggest alternatives, which is a gap given many sibling tools exist.

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 and destructiveHint=false, so the safety profile is clear. The description adds context about the data returned (risk score, lever scores, in-stock state, inventory, price per observation) but does not disclose potential rate limits, pagination, or the fact that results may be large. With annotations covering safety, this is adequate but not exceptional.

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 description is extremely concise at two sentences, front-loading the purpose in the first sentence. Every word adds value, with no unnecessary elaboration or repetition.

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?

Despite the complexity of time-series data across multiple dimensions (pincodes, dark-stores, window, multiple fields), the description covers all essential aspects. It mentions all key data points and guides the user to obtain the master ID. With an output schema present (not shown but indicated), return values need no further explanation.

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 input schema already describes all three parameters clearly. The description mentions 'window' which relates to dateRange but does not add new details beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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 the tool returns a time-series of shelf evaluations for one master product across pincodes and dark-stores, listing specific fields (risk score, lever scores, in-stock state, inventory, price). It distinguishes from siblings by focusing on a single master product and references list_qradar_skus as the source for the master ID.

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 tells when to use the tool (to get time-series data for a master product) and where to obtain the required masterId (from list_qradar_skus). It does not explicitly state when not to use it or compare against siblings like get_qradar_overview, but provides clear context for appropriate usage.

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 (readOnlyHint=true) are present; description adds valuable context about returning organic/sponsored presence in top 5/10/20 results per keyword and channel. No contradictions.

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 concise sentences: first defines the tool, second gives filtering options. No redundancy, front-loaded with core purpose.

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?

With output schema existing and annotations present, description covers what data is returned (organic/sponsored presence). Doesn't detail top 5/10/20 representation but output schema likely handles that. Sufficient for a read-only data 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 80% with descriptions for 4/5 params; description adds context for keyword and sortBy ('filter to one keyword or list the highest-organic'), supplementing enum values. Slightly above baseline.

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?

Description uses specific verb 'get' and resource 'share-of-search', clearly distinguishing from siblings like get_qradar_distribution. It specifies domain (quick-commerce) and scope (organic/sponsored presence in top results).

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?

Description provides usage hints: 'Filter to one keyword or list the highest-organic keywords.' It implies filtering/sorting but doesn't explicitly contrast with sibling tools like get_qradar_overview. Clear but lacks exclusions.

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 indicate read-only and non-destructive behavior. The description adds transparency about the aggregated view over a time window and that it returns top reimbursement reasons, which is not captured by annotations.

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?

Single sentence, front-loaded with the key purpose, no unnecessary words. Every part of the description adds value.

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?

Given low parameter count, optional input, existing output schema, and annotations, the description sufficiently explains the tool's functionality and outputs. It covers what the summary includes without being verbose.

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% for the single parameter (days). The description does not add significant new meaning beyond the schema's existing description of the lookback window.

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 the verb (get), resource (reimbursements summary), and the specific outputs (total amount, count, units, top reasons). It distinguishes itself from sibling summary tools like get_returns_summary by specifying focus on reimbursements.

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?

No explicit guidance on when to use this tool versus alternatives like get_returns_summary or get_payments_summary. The description does not provide context for exclusion or recommendations.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds context about the window parameter but does not disclose any other behavioral traits beyond what annotations provide.

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 description is a single sentence, front-loaded with key information, and every word is necessary. No wasted text.

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?

Given the low complexity, presence of output schema, and complete parameter documentation, the description provides sufficient context. No major gaps.

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 description coverage is 100% for the single parameter 'days', which is well-described. The description adds minimal extra meaning beyond the schema, hitting the baseline of 3.

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 the resource (Amazon returns) and the output (total count, total units, top return reasons and dispositions). It distinguishes itself from sibling tools like search_returns by focusing on summary statistics.

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 use for a high-level summary over a window but does not explicitly state when to use this tool versus alternatives like search_returns. No when-not guidance is provided.

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 and destructiveHint=false, so the safety profile is clear. The description adds the behavioral requirement of needing the SEO module, which is useful context. However, it does not mention any other traits like rate limits or return format, though an output schema exists.

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 description is a single, front-loaded sentence that conveys the purpose, source, and requirement without any extraneous words or redundancy. Every element earns its place.

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?

Given the tool's simplicity (one optional parameter), high schema coverage, presence of output schema, and annotations, the description adequately covers all essential information. It states the output metrics and module requirement, making it complete for an agent to use.

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 input schema provides full coverage (100%) for the single parameter 'range' with enum and description. The tool description does not add additional meaning beyond what the schema already defines, so it meets the baseline but does not exceed it.

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 identifies the tool as retrieving Google Search Console search queries with specific metrics (impressions, clicks, etc.) and states the required SEO module. However, it does not differentiate from similar sibling tools like 'get_serp_keywords', which might provide similar data.

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?

No guidance is provided on when to use this tool versus alternatives. The only context is the requirement of the SEO module, but there is no explicit statement about use cases or when not to use it.

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?

The description adds value beyond the annotations by detailing the specific data returned (rankings, volume, intent, trend). The annotations already indicate a read-only, non-destructive operation, and the description aligns without contradiction. No additional behavioral traits (e.g., rate limits, data freshness) are disclosed, but the core behavior is well-covered.

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 description is extremely concise, consisting of two sentences. The first sentence packs all core information (what is returned), and the second adds a critical prerequisite. No unnecessary words or repetition; every sentence earns its place.

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?

Given the tool's simplicity (one optional parameter, output schema exists), the description is largely complete. It covers the purpose and a prerequisite. However, it could briefly mention that the output schema defines the exact response structure, and it lacks guidance on the scope of 'tracked keywords' (e.g., all keywords in the SERP module?). Still, it is adequate for a simple 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?

The input schema has one parameter 'search' with a description 'Free-text keyword filter.' Since schema description coverage is 100%, the baseline is 3. The tool description does not provide additional context or examples beyond what is already in the schema, so no extra value is added.

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 the tool retrieves tracked SEO keywords with current rankings, search volume, intent, and trend. The title 'Get SERP keyword rankings' reinforces this. However, it does not explicitly distinguish itself from a potentially similar sibling like get_seo_queries, leaving room for ambiguity in tool selection.

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 only usage guidance is 'Requires the SERP module,' which is a prerequisite. No information is provided on when to use this tool versus alternatives or when not to use it, leaving the agent without context for decision-making among siblings.

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 and destructiveHint=false. The description adds behavioral interpretation ('Lower is worse') and scope details, which goes beyond annotations without contradiction.

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 description is two sentences long, front-loaded with the core purpose, and every sentence adds value (definition, interpretation, requirement). No wasted words.

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 parameterless tool with an output schema, the description fully explains what metric is returned, its interpretation, scope, and prerequisite. No gaps remain.

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?

Input schema has 0 parameters, so baseline is 4. The description correctly does not add parameter information since none exist, meeting the standard for tools with no parameters.

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 the verb 'get' and the resource 'shelf availability', defines the metric (OSA%) with scope ('over the last 7 days, per channel'), and distinguishes this tool from siblings by the specific Q-Radar context.

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 provides clear context ('Quick-commerce on-shelf availability') and a prerequisite ('Requires a Q-Radar subscription'), but does not explicitly mention when to use this tool over alternatives or when not to use it.

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 indicate readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds context about the specific data returned (taxable value, tax components, invoice count, state type) and the period parameter, enhancing understanding beyond the structured annotations.

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 description is a single sentence that efficiently conveys the tool's purpose and key data elements. Every part adds value, with no extraneous information.

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?

Given the presence of an output schema (not shown but indicated), the description adequately covers the tool's function, parameter, and data return. It is complete for a low-complexity, read-only tool, though it could optionally mention prerequisites like GST registration.

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% with a well-described enum parameter 'period'. The tool description only mentions 'for a period' without adding new details or constraints beyond the schema. Baseline 3 is appropriate as the schema carries the full burden.

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 specifies the verb (get) and resource (state-wise tax breakdown). It lists specific fields (taxable value, CGST/SGST/IGST, invoice count, intra/inter-state) and the context (per-state outward-supply for a period). This distinguishes it from sibling tools like get_tax_summary which likely aggregate at a higher level.

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 usage for retrieving state-level tax breakdowns within a period but does not explicitly state when to use this tool versus alternatives like get_tax_summary or get_gstr1_summary. No when-not-to-use or exclusion criteria are provided.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds what data is returned (OOS metrics, sample listings), providing useful behavioral context beyond the annotations.

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?

Single sentence that front-loads the key purpose and output details. Every element is necessary and concisely stated.

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 zero-parameter tool with an output schema, the description adequately explains what the tool returns. No additional information is needed for correct invocation.

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?

No parameters exist, so schema coverage is 100%. Per guidelines, 0 parameters earns a baseline score of 4.

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?

Clearly describes the tool's purpose: retrieving out-of-stock status across marketplace listings. It specifies the output details (total listings, OOS count, breakdown by channel, sample listings), distinguishing it from generic inventory tools.

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?

No explicit guidance on when to use this tool versus siblings like get_inventory or search_inventory. The description implies usage for OOS tracking but does not provide boundaries or alternatives.

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 indicate read-only and non-destructive behavior. The description adds context about return contents (drift amounts, workflow state) and default filtering, enhancing transparency without contradiction.

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 description is a single, well-structured sentence that front-loads the core function and adds only essential details. No wasted words.

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?

Given the tool's simplicity (1 optional parameter, output schema exists), the description is largely complete. It covers purpose, return fields, and default behavior. Minor omission: no mention of scope (e.g., time range or entity).

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 already fully describes the 'state' parameter with enum and default, and the description redundantly mentions 'Defaults to open items' without adding new meaning beyond 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 the tool identifies lines where stored tax doesn't equal rate times taxable value (drift audit), specifying the resource (lines) and the computation. It is distinct from sibling tools like get_tax_summary.

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 usage for tax drift auditing by describing the operation, but does not explicitly state when to use this tool over alternatives (e.g., get_tax_summary) or when not to use it.

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 indicate a safe read operation (readOnlyHint=true). The description adds no extra behavioral context such as side effects, authentication requirements, or rate limits.

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 description is a single sentence that packs in all relevant details (tax components, period scope, geographical context) without unnecessary words, though it could be slightly more structured.

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?

Given a single parameter fully covered by the schema, the presence of an output schema (removing need to describe returns), and explicit mention of India GST, the description is complete for its purpose.

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% with a single parameter having an enum. The description merely restates the enum options ('mtd', 'last-month', etc.) without adding new semantic meaning.

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 it provides a GST snapshot with specific tax components (CGST, SGST, IGST, cess, TCS, TDS) and invoice count split, distinguishing it from other reporting tools in the sibling list.

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 usage for a quick tax overview over common periods, but does not explicitly state when to use this versus other tax-related siblings like get_gstr1_summary or get_state_wise_tax.

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 and destructiveHint=false; description adds value by detailing returned data (balance, expiry, tiers) and unit conversion note.

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 concise sentences with no extraneous information, well-structured and front-loaded.

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?

Given the output schema exists, the description sufficiently covers the returned data and distinguishes from related tools, with no gaps.

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?

No parameters exist, so baseline is 4; description adds no param info but effectively describes the tool's output.

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 it retrieves the fulfillment-credit wallet balance, including free+paid, expiry, and recharge tiers, and distinguishes it from the AI wallet sibling.

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 usage by noting it is separate from the AI wallet, but lacks explicit when-to-use or when-not-to-use conditions.

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 and destructiveHint=false. The description adds behavioral context by specifying a time window for reporting and listing performance metrics, which goes beyond annotations and helps the agent understand the scope.

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 description is extremely concise: one sentence for purpose and one for prerequisite. No redundant information. Front-loaded with the main action and resource.

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 list tool with two optional parameters and output schema present, the description covers the main points: listing campaigns, performance metrics, time window, platform filter, and module requirement. It could mention default window or pagination, but overall complete given the context signals.

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?

Both parameters have schema descriptions, achieving 100% coverage. The description adds context about the requirement for the campaigns module but does not significantly enhance parameter meaning beyond the schema. Baseline 3 is appropriate.

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 it lists ad campaigns with per-campaign performance across platforms for a time window. It distinguishes from sibling list tools by specifying a distinct resource (campaigns) and adding performance metrics context.

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 mentions the prerequisite 'Requires the campaigns module' but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it. The usage context is implied but not fully explicit.

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 and destructiveHint=false, so the safety profile is clear. The description adds minor context about contents (severity, action plans) but no significant behavioral traits beyond annotations.

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 description is a single sentence, front-loaded with key information ('AI Coach recommendations inbox'), and contains no superfluous words.

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?

Given the low complexity (1 parameter, output schema present), the description adequately covers what the tool returns: issues/opportunities, severity, state, and action plans. No missing context.

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% with the one parameter (state) fully described. The description repeats the default ('open by default') but adds no new meaning beyond the schema. Baseline 3 is appropriate.

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 the tool lists AI Coach recommendations with details like severity, state, and action plans. It distinguishes this tool from siblings by specifying the unique resource (coach recommendations).

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 usage for viewing recommendations but does not explicitly state when to use this tool versus alternatives. No exclusions or context about when not to use it.

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 and destructiveHint=false, so the safety profile is clear. The description adds value by stating the data is 'Operator-curated', implying manual selection rather than automatic. However, no other behavioral traits (e.g., permissions, caching, pagination) are disclosed.

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 description is a single sentence of 10 words, front-loaded with the key information. Every word is informative, and there is no redundant or vague phrasing.

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?

Given the tool has no parameters and an output schema exists, the description is complete. It tells the agent what the tool returns (competitors with priority and status) without needing to explain return values in detail. No major gaps.

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?

There are no parameters, and schema coverage is 100% trivially. The description does not need to add parameter meaning. Baseline score of 3 is appropriate.

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 the tool lists tracked competitors with priority and status, matching the name 'list_competitors'. It is specific about the resource (competitors) and the attributes (priority and status), distinguishing it from siblings like list_campaigns or list_stores.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions. The description does not help an agent decide between this and similar list tools like list_campaigns or list_coach_recommendations.

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 indicate read-only and non-destructive behavior. The description adds valuable behavioral context by listing the specific fields returned (connection status, health score, last sync time), enhancing transparency beyond the annotations.

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?

Single sentence with front-loaded verb and resource, including relevant examples. No wasted words; efficient and clear.

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?

The description adequately covers the tool's output and purpose. Given the presence of an output schema and zero parameters, the description is sufficient for the agent to understand the tool's scope.

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?

With no parameters, the schema coverage is 100%. The description adds meaning by explicitly naming the output fields, compensating for the lack of parameter documentation.

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 explicitly states 'List the workspace's connected channels and data sources' with specific examples, making the purpose clear and distinguishable from sibling tools that deal with specific entities like orders or inventory.

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 usage for viewing integrations but provides no explicit guidance on when to use this tool versus alternatives (e.g., get_channel_orders_overview). No exclusions or alternative suggestions are given.

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 it as read-only and non-destructive. The description adds valuable behavioral context by specifying the manifest lifecycle stages included and that the tool returns summary counts and timestamps, which are not obvious from annotations alone.

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 description is two sentences, front-loading the core purpose and key outputs. Every word adds value with no redundancy or filler.

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?

Given the presence of an output schema (as per context signals), the description does not need to detail return values. It sufficiently covers input parameters, purpose, and filtering. For a list tool with optional parameters, it is complete.

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 high (80%), with descriptions for most parameters. The description reinforces the purpose of warehouse and date range filters, but does not explain offset/limit or their defaults, which are generic. Overall, it adds meaningful context beyond 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 it lists courier hand-over manifests with specific statuses (draft, submitted, acknowledged) and includes shipment/order counts and timestamps. It also mentions filtering capabilities, distinguishing it from sibling tools like get_manifest which likely returns a single manifest.

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 implicitly suggests use when needing a filtered list of manifests with summary counts, but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusion criteria or alternative tool names are provided.

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 and destructiveHint=false. The description adds that the result includes specific fields, going beyond annotations. No side effects or additional behaviors are disclosed, but they are not expected.

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?

Single sentence that is front-loaded and succinct. No wasted words; each part adds value.

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 simple list tool with two optional parameters and an output schema, the description is sufficient. It conveys the core purpose and contents, while schema handles parameter details.

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% with both parameters having descriptions. The tool description does not add extra meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 it lists warehouse pickers with specific fields (name, code, phone, assigned warehouse, active status). This distinguishes it from sibling tools like list_picklists or get_picklist.

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?

No explicit guidance on when to use this tool vs alternatives. The parameter descriptions imply some usage context (e.g., warehouseId to restrict), but no when-not-to-use or comparison to siblings.

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 confirm read-only and non-destructive behavior. The description adds context: the lifecycle stages of pick waves and what data is returned (line count, ship date, PDF existence), exceeding basic annotations.

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?

Single sentence, no redundant words, front-loaded with key concept 'Warehouse pick waves'. Could be slightly more structured but efficient.

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?

With output schema present and full schema coverage, description provides necessary context: states, included fields, and filtering option. Complete for a list endpoint.

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 schema already documents both parameters. Description mentions 'Filter by picklist state' but adds no extra meaning beyond schema for limit or state. No parameter-specific enrichment.

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 it lists warehouse pick waves with states ('draft → assigned → picking → completed') and specific fields (line count, ship date, PDF existence). It distinguishes from sibling 'get_picklist'.

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 does not explicitly say when to use this tool vs alternatives like 'get_picklist' or other list tools. It implies usage for listing picklists with optional state filter but lacks comparative guidance.

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 and destructiveHint=false, so the description's burden is lower. It adds context about alert types and affected entities, but does not detail behavioral traits like pagination, response size, or ordering. This is adequate but not exceptional.

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 description is a single sentence that efficiently conveys the core functionality with no filler. It begins with the subject 'Quick-commerce anomaly alerts' and lists relevant filtering dimensions, maximizing information density.

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?

With 5 parameters (including enums) and an output schema, the description lists the types of alerts and dimensions but does not explain how filtering works (e.g., default behavior, combination logic). While annotations and output schema fill some gaps, the description could be more specific about usage context.

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 description coverage is 60% (3 of 5 parameters have descriptions). The tool description does not add any additional meaning beyond the schema for the remaining parameters. With coverage above 50% but below 80%, baseline is 3, and no extra value is provided.

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 what the tool does: lists quick-commerce anomaly alerts of specific types (out-of-stock spikes, price anomalies, risk escalations, critical-stock events) with severity and affected entities. It distinguishes itself from sibling tools like get_qradar_distribution and list_qradar_skus.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or contextual triggers. Sibling tools like get_qradar_overview or list_qradar_skus serve different purposes, but no differentiation is given.

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 indicate this is a read-only, non-destructive, deterministic operation. The description adds value by disclosing the returned fields (OSA %, risk score, shelf counts, pricing) and sorting/filtering behavior. There is no contradiction 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the key purpose and fields, and introduces sorting and filtering without redundancy. Every word adds value.

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?

Given the tool has 8 optional parameters and an output schema, the description adequately covers the core use case and key actions. It explains when to use this tool versus other Q-Radar tools. The presence of an output schema means return values need not be detailed. Slightly more detail on the meaning of 'risk score' could improve completeness.

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 description is not required to repeat parameter details. It adds semantic context by explaining the purpose of sorting (worst-availability first) and filtering (own vs competitor), which goes beyond the schema descriptions. This justifies a score above the baseline of 3.

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 the tool lists SKUs in the Q-Radar rotation with specific attributes (OSA %, risk score, shelf counts, pricing). It mentions sorting by osa_asc for worst-availability and filtering by target OSA or ownership, which distinguishes it from sibling tools like get_qradar_overview or get_qradar_distribution.

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 provides guidance on when to use sorting (osa_asc) and filtering (under target OSA, own vs competitor). It implies the tool is for surfacing problematic SKUs. However, it lacks explicit when-not-to-use guidance or mentions of alternative tools for broader searches.

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 and destructiveHint=false, so the agent knows it's a safe read. The description adds context about the seller universe and classification, but does not disclose additional behavioral traits beyond what annotations provide.

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 description is a single, well-structured sentence that front-loads the core purpose and includes an optional filter. Every word is useful with no redundancy.

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?

Given the presence of an output schema and full annotation coverage, the description is sufficiently complete. It could briefly mention pagination behavior via limit, but the schema already documents that. Overall, it provides adequate context for this simple list 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 description does not need to add much. It mentions the channel filter but adds no new meaning beyond the schema. The limit parameter is not elaborated, but baseline 3 is appropriate.

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 the tool returns sellers from tracked listings with their classification and rating, including an optional channel filter. This distinguishes it from sibling tools like list_competitors, which likely focuses on competitor identification rather than the full seller universe.

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 usage for retrieving seller data but lacks explicit guidance on when to use this versus alternatives. It mentions an optional filter but does not specify when not to use or provide comparison with similar tools like list_competitors.

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 indicate readOnlyHint=true, destructiveHint=false, openWorldHint=false. The description adds value by specifying the exact fields returned (display name, country/currency, order count in last 30 days), which is beyond the annotations and enhances transparency.

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 description is a single sentence that immediately conveys the core purpose, followed by specifics. It is highly concise with no wasted words, front-loading the action and key details.

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?

Given the tool has no parameters and an output schema (presumably), the description covers all necessary aspects: what it lists, what data it returns, and it is complete enough for an agent to invoke correctly. No gaps.

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?

There are no parameters, so schema coverage is 100%. The description provides additional context about the output fields, which is useful beyond the schema. Baseline is 3 and the description justifies a 4 by enriching the understanding of what the tool returns.

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 the verb 'List' and the resource 'connected store/marketplace account', enumerating specific platforms (Amazon, Shopify, etc.) and the data fields returned. This makes the purpose unambiguous and distinguishes it from sibling tools which are either 'get_' or 'search_' for other entities.

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 implicitly tells when to use by specifying the tool lists all stores with certain details. While it doesn't explicitly mention when not to use or provide alternative tool names, the sibling tool context makes it clear this is the go-to for store listings, and the text is sufficient for an AI agent.

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 and destructiveHint false. The description adds behavioral context by specifying the required permission (org member-read), which goes beyond annotations. No contradictions.

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 sentences, no wasted words. Perfectly concise and front-loaded with the core purpose.

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?

Given the tool has no parameters and an output schema exists, the description is complete. It states the output (members with roles/permissions) and the required permission, which is sufficient for a simple list 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?

The input schema has no parameters, so schema coverage is 100%. The description does not need to add parameter information. Baseline for zero parameters is 4.

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 the tool lists workspace members with roles and permissions. It is specific and distinct from sibling tools that list other entities like campaigns or integrations.

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 usage for retrieving team members but does not explicitly state when to use this tool versus alternatives. It mentions a permission requirement (Owner/Admin) but lacks guidance on scenarios or exclusions.

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?

The description adds behavioral context beyond annotations: results are citeable with stable IDs and can be passed to 'fetch'. Annotations already declare readOnlyHint=true and destructiveHint=false, so the description enhances transparency without contradicting.

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 description is extremely concise—two sentences with no wasted words. The first sentence covers purpose and scope; the second adds behavior and linking. It is front-loaded and efficient.

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?

Given the low complexity (1 parameter, read-only, no nested objects), the description is complete. It covers what the tool does, what it searches, the nature of results, and how to use them with a sibling tool. The presence of an output schema further reduces the need for return value details.

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 sole parameter 'query' is fully described in the schema ('Free-text search over product title, brand, or SKU.'). The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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 the tool searches the workspace's product catalogue by free text on title, brand, or SKU. It distinguishes itself from sibling tools like search_catalog, search_inventory, etc., by specifying the product catalogue focus and mentioning citeable results with stable IDs.

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 explicitly states it is used by ChatGPT deep research and explains that results can be passed to 'fetch' for full detail. While it does not explicitly enumerate when not to use or list alternatives, the context is clear given the sibling tools.

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?

The description adds behavioral details beyond annotations: it returns up to 25 matches, implies truncation, and lists returned fields (MRP, channel count, status). This complements the readOnlyHint and destructiveHint annotations without contradiction.

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 concise sentences with no fluff. The first sentence covers purpose and outputs, the second provides usage guidance. Every sentence serves a clear function.

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?

Given the presence of an output schema, the description adequately covers scope, constraints, and hints. It is complete enough for an agent to decide to use and invoke correctly, though no additional caveats about empty results or edge cases are provided.

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 schema already documents both parameters. The description adds marginal value by reiterating the searchable fields and the default limit, but provides no new semantic information beyond 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 the tool searches the master product catalog by free text matching title, brand, or SKU, and returns specific fields. It differentiates from sibling search tools like search_inventory and search_orders by specifying the catalog scope.

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 advises narrowing the query if results are truncated, indicating best practices. It does not explicitly mention alternatives, but the context is sufficient for an agent to understand when to use this tool over others.

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 indicate read-only and non-destructive behavior. The description adds critical behavioral details: substring matching across multiple fields, master-grain results with per-channel/warehouse stock breakdowns, and health state inclusion. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences. The first sentence clearly states the main purpose, and the second provides essential details about matching and output. No superfluous words, every sentence earns its place.

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?

Given the tool's complexity (4 parameters, output schema with health states and inventory breakdown), the description explains the result grain, query matching, and filtering options. It covers the core behavior but could mention pagination or default limit behavior, though schema covers limit details.

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% with descriptions for all parameters. The description adds value by specifying that 'query' matches product name, SKU, brand, etc., as substring, which is not in the schema. It also explains the result structure, indirectly aiding parameter understanding.

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 the tool's function: 'Find inventory for a specific product across all channels.' It specifies the resource (inventory), the action (search/find), and details matching criteria and output structure, distinguishing it from sibling search tools like search_orders or search_catalog.

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 provides context on when to use the tool (searching inventory across channels with optional filters) but does not explicitly state when not to use it or direct to alternatives. The mention of filtering by channel or health state implies usage scenarios.

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?

The description adds significant behavioral context beyond the annotations (readOnlyHint, destructiveHint), including the lifecycle states, fulfillment type, credit status, and that it covers the zobship pipeline. No contradiction 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with key information (purpose and data fields), followed by filter guidance. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.