market

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

With no annotations provided, the description carries the full burden. It effectively discloses the output format ('Returns required kg, bags, and pallets') and reveals the calculation methodology via the explicit formula. Missing only operational details like rate limits or error conditions.

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?

Three sentences with zero waste: purpose first, output format second, implementation details third. The formula is included verbatim which is exactly what an agent needs to understand the calculation logic. No redundant or filler 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 4 well-described parameters and no output schema, the description compensates adequately by detailing the return values (kg, bags, pallets) and calculation logic. Could note that all parameters are optional (required: 0) but otherwise complete for a calculation utility.

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%, establishing a baseline of 3. The description adds value by explaining how parameters interact through the formula (kW * 350 * tempFactor * monthsFactor * insulationFactor), clarifying the calculation logic beyond individual parameter definitions.

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 provides a specific verb ('Calculate') and resource ('heating fuel needs') with clear input context ('from boiler specs'). It effectively distinguishes this from siblings like trade_corridor or salary_overview, which handle completely 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?

While there is no explicit 'when to use' guidance, the domain differentiation is obvious given siblings are unrelated (trade, soul, salary tools vs. heating fuel). The description implies usage through 'boiler specs' context but doesn't state prerequisites 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?

No annotations provided, so description carries full burden. Discloses return format ('quantities (bags, pallets) per location') and filtering behavior ('in-stock products' only). However, omits error handling (what if invalid product_id?), caching/real-time nature, or required permissions.

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?

Three sentences with zero waste: purpose (sentence 1), output format (sentence 2), parameter logic (sentence 3). Front-loaded with the most critical 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?

Adequate for a 2-parameter read-only tool with no output schema. Description compensates by specifying return format (bags/pallets) and default behavior when parameters omitted. Could mention error states but sufficient for the complexity level.

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%, establishing baseline 3. Description adds crucial semantic context that parameters are mutually exclusive alternatives and that omitting both returns all in-stock products—logic not present in the schema itself.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verb ('Check') + resource ('product stock') + scope ('across warehouse locations'). Distinguishes from siblings like 'search_products' (catalog search) and 'get_prices' (pricing) by focusing on inventory quantities.

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 clear parameter logic: 'Pass product_id or product_slug for a specific product, or omit both for all in-stock products.' This explains when to use specific filters vs. broad queries. However, lacks explicit contrast with sibling tools (e.g., doesn't mention using 'search_products' first to find IDs).

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?

With no annotations provided, the description carries full disclosure burden and succeeds admirably. It specifies the 21% VAT auto-calculation logic, discloses the required 'quote' scope API key for authentication, and explains the return values (quote number and pricing breakdown) despite the lack of an output schema.

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?

Four sentences efficiently cover purpose, calculation logic, return values, and authentication requirements. No redundancy or filler content; every clause provides essential information for agent decision-making.

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 absence of annotations and output schema, the description comprehensively covers all necessary context: the draft nature of the operation, specific VAT calculations, authentication requirements, and return value structure. Sufficient for safe and 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?

Schema description coverage is 100%, so the structured schema already documents all parameters including the items array structure and validation rules. The description mentions 'line items' but adds no semantic detail beyond what the schema already provides, which warrants the baseline score 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 opens with a specific verb ('Create') and resource ('draft quote'), and includes the local term '(piedavajums)' for clarity. It clearly distinguishes this from the sibling 'place_order' tool by emphasizing the 'draft' nature and quote-specific functionality.

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 'draft quote' designation and mention of VAT calculation provide clear context that this is for preliminary pricing rather than final orders. However, it does not explicitly reference sibling tools like 'place_order' or 'get_prices' to clarify when to use each alternative.

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?

No annotations provided, so description carries full burden. Discloses currency (EUR), calculated fields (price_per_kg), and filtering logic (in-stock only). Missing: read-only safety confirmation, error behavior for invalid slugs, pagination for 'all' results, and real-time vs cached data indicators.

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, zero waste. First sentence establishes action and return format; second establishes filtering scope and parameter behavior. Front-loaded with the most critical information (prices in EUR with calculation).

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?

Appropriate for a single-parameter read tool without output schema. Description compensates for missing output schema by detailing return contents (EUR values, calculated price_per_kg, in-stock filter). Could improve by noting pagination or default sorting for the 'all' case.

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%, establishing baseline 3. Description adds value by connecting 'slug' to the parameter and clarifying the 'all' behavior applies specifically to 'in-stock' products—context not fully captured in the schema's 'omit for all' text.

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?

Clear verb ('Get') + resource ('product prices') with specific scope ('in EUR', 'price_per_kg'). Implicitly distinguishes from sibling 'search_products' by focusing on price retrieval and from 'trade_price' by targeting general products. Lacks explicit sibling differentiation, which prevents a 5.

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?

Implies usage through 'Returns all in-stock products or a specific product by slug', hinting at when to omit the parameter. However, lacks explicit guidance on when to use this versus 'search_products' (text search) or 'trade_price' (trade-specific pricing), or prerequisites like product availability checks.

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?

With no annotations provided, the description carries the full burden. It discloses return values ('order details, product info, and delivery status') which compensates partially for the missing output schema, but fails to explicitly state whether this is a read-only operation, safe to retry, or idempotent.

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 consists of two efficient sentences with zero waste: the first establishes purpose and the second discloses return values. It is appropriately front-loaded and sized for a single-parameter tool.

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 low complexity (1 parameter, no nested objects) and absence of an output schema, the description adequately compensates by detailing what data is returned. It lacks error handling documentation (e.g., behavior when order ID doesn't exist) but is otherwise 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?

The schema has 100% description coverage for its single parameter ('Order ID (required)'). The description mentions 'by ID' which aligns with the schema but adds no additional semantic context about ID format, where to obtain it, or validation rules. 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 states a specific action ('Check') and resource ('order status') with scoping ('by ID'). It implicitly distinguishes from siblings like 'place_order' (creation) and 'check_availability' (different resource), though it doesn't explicitly contrast with them.

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 what the tool does but provides no guidance on when to use it versus alternatives like 'place_order' or 'search_products', nor does it mention prerequisites such as needing a valid order ID from a prior purchase.

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?

No annotations provided, so description carries full burden. It discloses critical behavioral traits: EUR 50,000 financial limit, idempotency semantics for duplicate prevention, and API key scope requirements. Missing output behavior description but covers constraint and mutation behavior well.

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?

Perfectly front-loaded with purpose first, followed by constraints and requirements. Five sentences, zero waste: purpose, identification requirement, financial limit, idempotency feature, and auth scope each earn their 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?

For a 10-parameter transactional tool with no output schema, description adequately covers input semantics, business rules (50k limit), and auth. Minor gap: does not describe return values or success/failure behavior, though schema coverage compensates for input side.

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 (90%), establishing baseline 3. Description adds crucial value by clarifying the XOR relationship between product_id and product_slug ('at least one') and introducing the EUR 50,000 limit constraint not present in schema 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?

Description opens with specific verb+resource ('Place a product order') and clearly distinguishes from siblings like create_quote (which creates estimates) or order_status (which queries state) by being the definitive order creation action.

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 clear prerequisites (product_id or product_slug required), mentions idempotency_key for duplicate prevention, and specifies auth requirements ('order' scope). Lacks explicit 'use create_quote first' guidance but covers critical operational constraints.

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?

Without annotations, the description carries the full burden of behavioral disclosure. It provides valuable scale context (28M+ records, 27 EU countries) but omits critical operational details such as rate limits, response format, caching behavior, or whether the data is real-time vs historical. The mention of HS2-CN8 codes hints at data structure but lacks completeness.

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 consists of two highly efficient sentences with zero waste. It front-loads the data source identification (Eurostat COMEXT) in the first sentence and enumerates capabilities in the second. Every word earns its place; there is no fluff 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 the tool's complexity (8 parameters, 9 query types, 28M+ record dataset) and lack of output schema, the description provides minimum viable context. It identifies the query types but does not explain their semantics, return structures, or appropriate use cases. For a complex analytical tool, additional context on result formats or query type selection would be necessary for a higher score.

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 description coverage, the baseline is 3. The description adds value by enumerating the query_type options (overview, countries, timeline, etc.) in the text, which aids in understanding available analysis modes, and clarifies that HS2 codes relate to wood products (e.g., '44' for wood), enhancing the schema 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 clearly identifies the tool as querying EU trade data from Eurostat COMEXT DS-045409, providing specific dataset identification. However, it does not explicitly distinguish its use case from sibling trade tools (trade_forecast, trade_alerts, trade_corridor, etc.), leaving ambiguity about which trade tool to select for specific analytical needs.

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 the numerous sibling trade tools (trade_forecast, trade_macro, etc.) or other data sources. It lists query types but offers no selection criteria for choosing between 'overview' vs 'heatmap' or guidance on required parameter combinations.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conceptual output ('shows which jobs/sectors are most at risk') but fails to specify output format, data granularity, whether results are ranked, or if the operation is read-only/safe, leaving significant behavioral gaps.

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 consists of two efficient sentences with no redundancy. It immediately establishes the tool's function ('AI automation exposure analysis') in the first sentence and clarifies the output intent ('shows which jobs/sectors...') in the second.

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 absence of an output schema, the description should specify the return format or structure; instead it only vaguely states it 'shows' risk information. It adequately covers the tool's conceptual domain but leaves operational details (default behavior for empty params, output structure) unspecified.

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%, establishing a baseline of 3. The description mentions ISCO and NACE codes, reinforcing the schema definitions for 'occupation' and 'sector', but adds no additional semantic context for the 'country' parameter or guidance on valid input formats 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 identifies the tool performs 'AI automation exposure analysis' using specific classification systems (ISCO occupation, NACE sector) to identify displacement risk. This effectively distinguishes it from sibling salary tools (salary_wages, salary_overview) by specifying the AI risk focus.

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?

While the description mentions the analysis dimensions (ISCO/NACE), it provides no explicit guidance on when to use this tool versus other salary-related siblings, nor does it explain the implications of all parameters being optional (required: 0) or what default behavior to expect.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully lists the data points returned (countries, sectors, years, counts) but omits other behavioral traits like caching, rate limits, or whether the operation is read-only.

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, efficiently structured sentence using an em-dash to separate the high-level purpose from specific details. Every clause conveys essential information about the scope of returned data 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 absence of an output schema, the description adequately compensates by listing the specific data categories returned (countries, sectors, years, counts). For a zero-parameter metadata tool, this provides sufficient context for invocation, though it doesn't specify the response format structure.

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, which per the baseline rules warrants a score of 4. The description appropriately does not invent parameters that don't exist in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource (EU salary database) and the specific information returned (countries, sectors, coverage years, record counts). It implicitly distinguishes from sibling 'salary_wages' by focusing on metadata rather than actual wage data, though it doesn't explicitly name alternatives.

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 like 'salary_wages' or 'salary_ai_risk', nor does it mention prerequisites or filtering capabilities. A user must infer that this is a discovery/metadata 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?

Adds valuable provenance context ('Actual salary statistics from official sources') not found in annotations or schema. However, with no annotations provided, it fails to disclose operational details like pagination, rate limits, or whether the data is aggregated or raw.

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?

Extremely efficient two-sentence structure. First sentence establishes scope and dimensions; second sentence establishes data quality/provenance. No redundant or filler 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?

Adequate for a simple 3-parameter query tool with no output schema. It explains what data is returned and its source, but lacks information about response structure, pagination, or time range limits that would help the agent handle results.

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 100% description coverage (year, region, sector). The description mentions these dimensions but adds no additional semantic value regarding valid formats (e.g., NACE code syntax) or constraints. Baseline score applies since schema is self-documenting.

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 states it retrieves detailed Latvia wage data broken down by sector, occupation, and region. The 'detailed' qualifier and geographic specificity help distinguish it from sibling tools like salary_overview and salary_ai_risk, though it could explicitly name these alternatives.

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 no guidance on when to use this tool versus the other salary-related siblings (salary_overview, salary_ai_risk). Does not mention prerequisites like valid year ranges or supported region/sector values.

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?

With no annotations provided, the description carries full burden of behavioral disclosure. It compensates partially by listing returned fields (name, price, type, availability) and noting the 'in-stock' constraint. However, it omits critical behavioral traits: whether results are paginated, return format (array vs object), read-only safety status, or rate limits. 'Search' implies read-only, but explicit confirmation is absent.

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?

Three sentences with zero waste. Front-loaded with core purpose ('Search in-stock products'), followed by return value disclosure, then parameter guidance. Each sentence earns its place: sentence 1 defines scope and returns, sentence 2 covers filtering options, sentence 3 explains the lookup shortcut. No redundant phrases or repetition of schema 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?

For a 3-parameter search tool with 100% schema coverage and no output schema, the description adequately compensates by listing return fields. The specific examples for the type parameter and the explicit mention of return fields provide sufficient context for invocation. Minor gap: does not specify whether results are returned as an array or single object, though this can be inferred from the 'single-product lookup' phrasing.

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%, establishing baseline 3. Description adds significant value by enumerating specific valid type values ('granulas, briketes, saplaksnis') that the schema only lists as 'e.g.' examples. It also clarifies the functional distinction between parameters: 'slug for single-product lookup' versus general filtering, adding semantic meaning beyond the schema's technical descriptions.

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 opens with specific verb ('Search') and resource ('in-stock products in the catalog'), immediately clarifying scope. The phrase 'in-stock' effectively distinguishes it from sibling tools like get_prices or check_availability by implying full catalog browsing capability. Mentioning return fields (name, price, type, availability) further clarifies its comprehensive data retrieval purpose versus specialized siblings.

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 clear internal usage context by distinguishing three invocation patterns: filter by type, name search, and slug-based single-product lookup. Explicitly states 'Pass slug for single-product lookup,' guiding the agent on when to use which parameter combination. Lacks explicit comparison to sibling tools (e.g., 'use this instead of get_prices when you need full product details'), preventing a score of 5.

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?

No annotations are provided, placing full burden on the description. The text fails to disclose computational cost, caching behavior, rate limits, or whether these analytics are real-time vs. batched. It also doesn't clarify what 'behavioral drift' means in this domain or the format of the returned data (graph structure vs. tabular).

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, efficient sentence using an em-dash to list capabilities. No filler words or redundant phrases. Front-loaded with the core subject 'Agent analytics' immediately identifying the domain.

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?

Adequate for basic invocation given the simple schema, but gaps remain: it fails to mention that agent_id is conditionally required (per schema: 'required for drift'), and with no output schema provided, the description should hint at the varying return structures (dashboard vs. graph vs. detection report) for the four analysis types.

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, the baseline is adequate, but the description adds valuable semantic mapping by associating enum values with their outputs (e.g., 'roi' produces an 'ROI dashboard', 'collaboration' produces a 'graph'). This helps the agent understand the intent behind each type option beyond the raw schema labels.

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 identifies the resource (Agent analytics) and lists four specific capabilities (ROI dashboard, collaboration graph, drift detection, failure analysis). However, it does not explicitly differentiate from sibling 'soul_' tools like soul_insights or soul_profile, which could help with 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?

Provides no guidance on when to use this versus sibling analytics tools (soul_insights, soul_profile) or which of the four analysis types to select for specific use cases. Also omits the conditional requirement noted in the schema that agent_id is specifically required for drift analysis.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure and adds valuable context about the badge calculation criteria (trust score, activity, validations). However, it fails to disclose other critical behavioral traits such as whether the operation is read-only, error handling behavior for invalid agent IDs, or rate limiting concerns.

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, efficiently structured sentence of 12 words that front-loads the core action ('Get earned reputation badges') before adding qualifying context via an em-dash. There is no redundant or wasteful language, making it appropriately sized for the tool's simplicity.

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 low complexity (single string parameter, no nested objects), the description combined with the schema adequately covers the input requirements, though it lacks information about the return format or structure since no output schema exists. For a retrieval tool, it meets minimum viability but could improve by indicating whether the response is a list, object, or empty when no badges exist.

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 with the 'agent_id' parameter already documented as 'Agent identifier', establishing a baseline score. The description reinforces this by mentioning 'for an AI agent' but does not add specific format requirements, examples, or constraints beyond what the schema already provides.

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 uses a specific verb ('Get') and resource ('earned reputation badges') to clearly indicate the tool retrieves badge information for AI agents. While it effectively identifies the target resource, it does not explicitly differentiate from sibling tools like 'soul_profile' or 'soul_verify' that may overlap in agent reputation 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 explains what data the badges are based on ('trust score, activity, validations') but provides no explicit guidance on when to use this tool versus the numerous sibling 'soul_' prefixed alternatives. There are no stated prerequisites, exclusions, or workflow recommendations for agent selection.

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?

With no annotations provided, the description carries the full burden. It establishes the domain (claimable tasks) and implies read-only behavior through the verb 'List,' but omits operational details like pagination behavior, rate limits, or the structure of returned bounty data.

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, efficient sentence (11 words) that front-loads the action ('List open bounties') and uses an em-dash to add clarifying context without verbosity. Every word 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?

For a simple listing tool with 100% schema coverage and no output schema, the description is appropriately complete. It successfully explains the domain concept (bounties as claimable tasks), though it could slightly improve by noting that listing is distinct from claiming (implied but not explicit).

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%, establishing a baseline of 3. The description mentions 'open bounties,' which aligns with the status parameter's default value, but does not add syntactic details, validation rules, or semantic relationships between parameters beyond what the schema already provides.

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 uses a specific verb ('List') and resource ('bounties'), clearly targeting 'AI agents' as the user. It distinguishes itself from sibling tools (soul_profile, soul_badges, etc.) by uniquely focusing on 'tasks with rewards that agents can claim.'

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?

While the description implies usage context by characterizing bounties as 'tasks with rewards that agents can claim,' it lacks explicit guidance on when to use this versus other soul_* tools (like soul_profile for viewing claimed bounties) or prerequisites for claiming.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully describes what the report contains (the four components listed), providing context about the output structure. However, it omits operational details such as whether the report is generated in real-time, cached, requires specific permissions, or how 'DNA profile' should be interpreted in this regulatory 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 a single, dense sentence with no wasted words. The information is front-loaded with the regulatory domain (EU AI Act Article 12) immediately establishing context. The em-dash list efficiently conveys the report components, though the density slightly hinders readability.

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 single parameter (fully documented in schema) and lack of output schema, the description partially compensates by listing the report's content areas. However, it lacks completeness regarding the return format (structured object vs. PDF-like blob) and does not clarify whether the 'DNA profile' refers to the agent's configuration or something else, leaving gaps for a regulatory compliance 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 100% description coverage ('Agent identifier'), establishing a baseline score of 3. The description does not mention the agent_id parameter or provide any additional context about what constitutes a valid agent identifier, relying entirely on the schema 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 clearly identifies the output as an EU AI Act Article 12 compliance report and specifies its four components (trust trajectory, event summary, DNA profile, regulatory mapping). It implicitly distinguishes from sibling 'soul_compliance_check' by emphasizing the detailed reporting aspect versus a simple check. However, it lacks an explicit action verb (e.g., 'Generates' or 'Retrieves').

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 similar siblings like 'soul_compliance_check' or 'soul_analytics'. There are no prerequisites, exclusion criteria, or workflow positioning hints to help the agent decide if this is the appropriate compliance tool for a given user request.

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?

No annotations are provided, so the description must carry full behavioral disclosure. It states the tool produces 'reports' but does not clarify if this generates new assessments, retrieves cached results, triggers async processing, or requires specific permissions. Lacks disclosure on side effects, idempotency, 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?

Extremely concise single-sentence structure with no filler. Front-loads the domain (EU AI Act) immediately, followed by the specific report variants. Every word conveys essential information about scope or report type.

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?

Inadequate given the lack of output schema and annotations. The description does not explain what the returned report contains, when 'agent_id' is mandatory (hinted as 'optional for some reports' in schema but unexplained), or what distinguishes a 'full bundle' from individual components. A compliance tool requires more behavioral 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?

While the schema has 100% coverage, the parameter descriptions are minimal ('Report type', 'Agent identifier'). The description adds crucial semantic context that these are 'EU AI Act' reports and maps cryptic enum values (e.g., 'annex-iv') to human-readable concepts ('Annex IV'), significantly aiding agent comprehension.

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 identifies the domain (EU AI Act) and specific report outputs (risk classification, Annex IV/V, etc.). Lists the five report variants available. However, it fails to distinguish from the sibling tool 'soul_compliance', leaving ambiguity about which compliance tool to select.

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 no guidance on when to use this tool versus 'soul_compliance' or other siblings. Does not explain when the optional 'agent_id' parameter is required versus when it can be omitted, nor which report type is appropriate for specific compliance 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context about data provenance ('published by AI agents') and ranking ('quality scores'), but omits operational details such as pagination behavior, caching policies, or rate limits that would help the agent manage the interaction.

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 consists of a single, efficient 12-word sentence that leads with the action verb and contains zero redundancy. Every word earns its place, providing maximum information density without 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 absence of an output schema, the description partially compensates by conceptually describing the return value (insights with quality scores). However, it lacks structural details about the response format and pagination mechanics, leaving gaps for a browsing tool with optional parameters.

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%, with 'limit' and 'category' fully documented in the structured schema. The description adds no parameter-specific semantics, but this is acceptable given the self-documenting nature of the schema fields. Baseline score appropriate for high schema coverage.

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 uses specific verb 'Browse' with clear resource 'marketplace insights' and characterizes the content as 'analysis findings with quality scores.' This helps distinguish it from sibling 'soul_analytics' by emphasizing the curated, scored nature of the data, though it doesn't explicitly state when to choose one over the other.

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 like 'soul_analytics' or 'soul_profile'. It lacks prerequisites, conditions for use, or exclusion criteria that would help an agent determine if this is the correct tool for the user's intent.

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?

No annotations provided, so description carries full burden. It discloses the conceptual content of the response (archetypes, event counts) which helps anticipate output structure, but omits safety traits (read-only vs destructive), caching behavior, 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?

Extremely concise single phrase with no redundancy. Information density is high though brevity sacrifices some necessary context like action verbs and sibling differentiation.

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 single-parameter tool without output schema, the description partially compensates by listing return data fields. However, it lacks domain context regarding how 'trust scores' are calculated or when this leaderboard view is appropriate versus individual profile lookups.

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 100% description coverage for the single 'limit' parameter. The description adds no parameter-specific guidance, but the schema adequately documents the parameter without additional description support, meeting baseline expectations.

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?

Identifies the resource as an 'Agent trust leaderboard' and mentions output fields (trust score, archetypes, event counts), but lacks an explicit action verb (retrieve/list/get) and does not clarify ambiguous term 'Agent' (human representative? AI agent?) or differentiate from sibling tools like soul_profile or soul_analytics.

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 no guidance on when to use this tool versus alternatives such as soul_profile, soul_analytics, or soul_insights. No prerequisites, filtering guidance, or exclusion criteria 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?

With no annotations provided, the description carries the full burden of behavioral disclosure but fails to mention side effects, authentication requirements, rate limits, or error handling (e.g., invalid agent_id). It only describes the conceptual payload returned, not the tool's operational behavior.

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 with zero waste. Every clause earns its place: the verb establishes the action, the noun phrase establishes the resource, and the em-dash list clarifies the specific data domains returned.

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 absence of both annotations and an output schema, the description compensates partially by listing conceptual return fields (trust score, etc.), but leaves significant gaps regarding return structure, data formats, and error scenarios. Adequate for a single-parameter tool but minimum viable given the lack of structured metadata.

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 'agent_id' parameter, establishing a baseline score of 3. The description adds no explicit parameter guidance, but none is needed given the schema's completeness and the examples provided ('oracle', 'spider', 'diplomat').

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 uses a specific verb ('Get') and resource ('AI agent identity profile') and uniquely distinguishes itself from sibling tools by enumerating distinct components: 'trust score, behavioral DNA, character model, chain integrity.' This clearly positions it as the core identity retrieval tool versus adjacent soul_* tools like soul_analytics or soul_badges.

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?

While the description implies usage through the specific profile components listed (suggesting this tool retrieves static identity attributes versus dynamic analytics or compliance status), it provides no explicit guidance on when to prefer this over siblings like soul_insights or soul_verify, nor does it mention prerequisites or error 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context about 'trust scores' associated with insights, but fails to explicitly state the read-only/safe nature of the operation, pagination behavior, or rate limiting. The term 'browse' implies safety, but explicit confirmation is absent given the annotation gap.

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 appropriately concise with two efficient sentences that front-load the primary action and resource. The em-dash effectively separates the core function from the content description, and the second sentence earns its place by highlighting key filter capabilities without verbosity.

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 tool with four optional parameters and no output schema or annotations, the description adequately covers the primary function and filter support. However, it lacks completeness regarding the return data structure (e.g., that it returns a feed/list) and safety characteristics that would help an agent understand the tool's impact, which is particularly important given the absence of annotations.

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, establishing a baseline score of 3. The description mentions 'trending and category filters' which reinforces the schema's filter parameters, but adds no additional semantic detail about the 'agent_id' or 'limit' parameters, nor does it provide format examples or value constraints beyond what the schema already provides.

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 core action ('Browse') and specific resource ('SoulLedger Stack feed'), and distinguishes it from sibling tools by specifying the 'Stack' feed type versus other soul_* features like analytics or bounties. It additionally clarifies the content type ('published insights from AI agents with trust scores') which helps identify the tool's domain.

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

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

While the description mentions supported filters ('trending and category filters'), it provides no guidance on when to use this tool versus sibling alternatives like soul_insights or soul_analytics. There are no explicit prerequisites, exclusions, or scenarios indicating when this feed browsing is preferred over other data retrieval options.

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?

No annotations provided, so description carries full burden. Adds valuable context about 'cryptographic proof' and 'untampered' nature indicating security-focused read-only operation, but omits failure modes, output format (boolean vs report), or side effects.

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 dense sentence with action front-loaded. Zero redundancy — every word ('cryptographic', 'untampered', 'integrity') adds distinct meaning beyond the tool name.

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?

Adequate for a single-parameter tool despite lacking annotations and output schema. Cryptographic context is established, though disclosure of return value structure (pass/fail indicator vs detailed chain data) would strengthen 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 has 100% coverage with 'Agent identifier' description. The tool description provides no additional parameter guidance (format examples, validation rules), warranting the baseline score for well-documented schemas.

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?

Excellent specificity with 'Verify agent hash chain integrity' — clear verb, technical resource, and distinct from siblings like soul_analytics or soul_profile which handle data retrieval rather than cryptographic validation.

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 invoke this versus similar verification tools like soul_compliance_check, or prerequisites for the agent_id. Description states what it does but not selection criteria.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context by specifying the tool looks for 'significant spikes or drops,' but fails to disclose safety properties (read-only vs. destructive), output format, or detection thresholds.

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, efficient sentence that front-loads the key information (anomaly detection) without redundant words. Every word 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?

For a 4-parameter analysis tool with no output schema or annotations, the description adequately explains the core function but leaves gaps regarding return value structure, anomaly criteria, and how it complements other trade analysis tools in the suite.

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% (three of four parameters have descriptions). The description adds no additional parameter context beyond what the schema provides, but the baseline of 3 is appropriate given the relatively high schema coverage.

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 performs 'anomaly detection in trade data' and identifies 'spikes or drops in trade volumes or values,' providing a specific verb and resource. However, it does not explicitly differentiate from sibling trade analysis tools like trade_forecast or trade_concentration.

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 (e.g., when to choose anomaly detection over forecasting or price analysis). There are no stated prerequisites, exclusions, or conditions for use.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully explains that the tool calculates HHI to measure concentration/diversification of trade partners, but lacks operational details such as data coverage limitations, handling of missing years/countries, or the output value range (e.g., 0-10,000 scale typical for HHI).

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 consists of two efficient sentences with zero redundancy. It front-loads the analytical method (HHI) in the first sentence and interprets the practical meaning (concentrated vs. diversified) in the second, making optimal use of limited space.

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 4-parameter analytical tool without output schema or annotations, the description adequately covers the domain concept (HHI) and its application to trade data. It could be improved by noting the index scale or interpretation thresholds, but it provides sufficient context for an agent to understand what analysis will be performed.

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?

While the schema has 75% coverage with decent descriptions, the description adds valuable domain context by clarifying that parameters define a specific trade relationship (product via hs2, direction via flow, country via reporter) for analyzing 'trade partners' concentration. This semantic framing helps agents understand the parametric relationships beyond individual field definitions.

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 the tool performs 'Market concentration analysis using HHI (Herfindahl-Hirschman Index),' providing a specific method and domain. It distinguishes itself from siblings like trade_corridor or trade_forecast by focusing specifically on partner concentration/diversification metrics rather than routes or predictions.

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 what the tool does but provides no guidance on when to select it over alternatives like query_trade (general data retrieval) or trade_corridor (bilateral route analysis). There are no prerequisites, exclusions, or explicit use-case scenarios 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?

No annotations provided, so description carries full disclosure burden. It successfully indicates return data types (timeline, products, balance) but omits operational details like rate limits, caching behavior, or data granularity/vintage that would help an agent plan invocation strategy.

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 waste. First sentence defines action and scope, second specifies outputs. Efficiently front-loaded with no filler or redundant 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?

Adequately compensates for missing output schema by describing conceptual return values (timeline, products, balance). For a 4-parameter data retrieval tool, the description covers functional scope sufficiently, though data source attribution or granularity notes would improve it further.

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% (3 of 4 parameters documented). Description mentions 'between two countries' which implicitly maps to reporter/partner semantics but does not explain the flow enum or year format. Baseline score appropriate since schema does 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?

Specific verb ('deep-dive') and resource ('bilateral trade corridor') clearly distinguish this from sibling tools like trade_macro (aggregate) or trade_forecast (predictive). Specifies exact outputs (timeline, top products, trade balance) that differentiate it from generic query_trade.

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?

Implies usage context through 'bilateral' and 'between two countries,' suggesting use for two-country analysis. However, lacks explicit guidance on when to use versus trade_macro, trade_concentration, or query_trade 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?

With no annotations provided, description carries full disclosure burden but only offers minimal methodology hint ('Simple'). Fails to disclose projection horizon (how many future years?), accuracy limitations, data freshness requirements, or behavior with insufficient historical data.

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 functionality. No redundant or wasted text; appropriately terse for the information provided.

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?

Lacks output schema and annotations, yet description fails to explain return format (projected values? confidence intervals? time periods?), forecast limitations, or data source constraints. Insufficient for a forecasting tool's operational 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 75% (3/4 params described). Description adds no parameter context, leaving 'flow' parameter undocumented beyond enum values and providing no format guidance for 'years' range syntax. Baseline score since schema does 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?

States specific function (trend-based forecast for trade volumes) and methodology (historical data projection). Lacks explicit differentiation from siblings like trade_macro or trade_seasonality, though 'forecast' implies predictive vs. analytical 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?

Provides no guidance on when to use versus alternatives (e.g., trade_macro for macroeconomic forecasting), prerequisites for historical data sufficiency, or when projections may be unreliable.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It lists the data returned but fails to indicate if the operation is read-only, idempotent, or if there are rate limits/data freshness concerns.

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, efficient sentence that front-loads the core concept. No redundant words; the em-dash list format appropriately summarizes the indicator types without verbosity.

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 output schema exists, the description adequately compensates by enumerating the specific indicators returned (GDP, population, trade openness). Sufficient for a simple 2-parameter lookup 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 description coverage is 100% (year and reporter are fully documented). The description implies the country context but does not add specific parameter semantics beyond what the schema already provides, meeting the baseline for high-coverage schemas.

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 provides macro-economic context (GDP, population, trade openness) for trade analysis. However, it does not explicitly differentiate from sibling trade tools like trade_concentration or trade_corridor.

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 provided on when to use this tool versus alternatives like trade_forecast or trade_corridor. No mention of prerequisites or when it is inappropriate to use.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While it specifies the currency/unit (EUR/kg), it fails to disclose data freshness (real-time vs. historical), safety/idempotency characteristics, rate limits, or error conditions (e.g., missing data scenarios).

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 consists of two efficient sentences with zero redundancy. The first sentence front-loads the core action and parameters, while the second provides use-case context—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?

For a 5-parameter data retrieval tool without output schema or annotations, the description adequately covers the domain (EU trade prices) but leaves gaps regarding return value structure, data granularity (monthly vs. yearly), and coverage limitations.

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 has 100% description coverage, establishing a baseline of 3. The description references key parameters ('HS product code', 'reporter country', 'year') but does not add validation rules, cross-parameter dependencies, or formatting nuances beyond what the schema already provides.

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 specific action ('Get'), resource ('price per tonne'), and scope ('HS product code, reporter country, and year'). The EUR/kg unit and HS code specificity effectively distinguish it from generic sibling tools like 'get_prices' or 'trade_forecast'.

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 notes the tool is 'Useful for benchmarking and price comparisons across EU markets,' providing implied context. However, it lacks explicit guidance on when to prefer this over siblings like 'query_trade' or 'trade_forecast', and does not state prerequisites 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully indicates temporal granularity ('Monthly') and analysis type ('peaks and troughs'), but omits critical operational details like data source limits, caching behavior, or whether the operation is read-only and safe to invoke freely.

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 consists of two efficient sentences with zero redundancy. The first sentence establishes the core resource and scope (monthly patterns), while the second clarifies the analytical output (peaks/troughs), front-loading the most critical 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 4-parameter data tool with no output schema, the description adequately conveys the analytical intent but leaves gaps regarding return value structure, pagination, or error conditions. Given the richness of sibling trade tools, additional context on how this fits into the analysis workflow would 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 75% (three of four parameters have descriptions). The description maps parameters to business concepts ('country and product'), but does not add syntax details, validation rules, or semantic clarifications beyond what the schema already provides for the 'flow' enum or year formatting.

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 analyzes 'Monthly import/export patterns' and identifies 'seasonal peaks and troughs,' providing specific verb and resource. However, it does not explicitly differentiate from siblings like 'trade_forecast' or 'trade_macro,' which also analyze trade patterns.

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 like 'trade_forecast' (predictive) or 'query_trade' (general data retrieval). There are no stated prerequisites, exclusions, or workflow recommendations.

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