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

The description discloses the data source (World Bank API) and the output fields, which is helpful. However, it does not mention error handling, data freshness, or that the operation is read-only (no annotations provided). The description partially compensates for missing annotations but lacks depth.

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, with an 'Args:' section clearly listing parameters and descriptions. However, being in German may reduce usability for an English-speaking AI agent. Every sentence adds value, but the language choice is a minor drawback.

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 tool (2 parameters, no output schema), the description covers the main purpose and return fields. However, it lacks details on output format, units, error scenarios, and data availability constraints, making it adequate but not thorough.

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 0%, but the description adds examples for 'country' (e.g., 'deutschland') and notes 'year' is the data year. This provides some semantic value, but does not specify allowed formats, list valid countries, or indicate that the year has a default of 2022.

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 retrieves general trade statistics for a country via the World Bank API, specifying the return fields (exports, imports, trade balance, trade-to-GDP ratio). It distinguishes from siblings like 'get_top_trade_partners' by focusing on overall stats, but does not explicitly contrast 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?

No guidance is provided on when to use this tool vs. alternatives (e.g., 'get_trade_flow' for specific flows). There are no conditions, exclusions, or recommendations, leaving the AI to infer usage 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?

No annotations provided. The description does not disclose any behavioral traits beyond basic operation, such as permissions, rate limits, 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?

The description is concise and front-loaded with the purpose. It efficiently lists parameters and return values, though the Args and Returns formatting adds structure.

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?

Without an output schema, the description outlines return components (concentration, critical dependencies, alternative sources). This is sufficient for a 2-parameter tool with no enums.

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 0%, but the description adds meaning by explaining 'product' as 'Produkt oder Branche' with examples and 'analysis_depth' options as 'basic' or 'detailed'. This compensates for the schema's lack of 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?

The description clearly states the tool analyzes supply chain risks for a product, with specific parameters and return values. It is distinct from sibling tools focused on trade statistics and suppliers.

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. The description does not mention prerequisites or context for 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?

Without annotations, the description bears full responsibility. It indicates a read operation returning a ranking with trade volumes, but does not disclose behavior for invalid inputs, data freshness, or result limits. Basic transparency is achieved.

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, using a short sentence and bullet-like parameter list. No redundant information, and the purpose 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 no output schema and no annotations, the description should provide more detail on the return format and edge cases. It only states 'Ranking der wichtigsten Handelspartner mit Handelsvolumen', which is adequate but not fully complete. Could be improved by specifying the ranking order or data 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?

Schema description coverage is 0%, so the description must add meaning. It provides clear explanations for each parameter: 'Land' for country, 'Jahr der Daten' for year, 'import oder export' for flow, and 'Anzahl Top-Partner' for limit. This effectively compensates for the missing schema 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 clearly states the tool returns top trade partners for a country, with a specific verb ('Gibt zurück') and resource. It is distinct from siblings like 'get_country_trade_stats' (broader stats) and 'search_suppliers_by_country' (supplier search). However, no explicit differentiation is made.

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 like 'get_trade_flow' or 'get_country_trade_stats'. The description lacks contextual exclusions or recommendations, leaving the agent to infer 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?

No annotations exist, and the description does not disclose behavioral traits like read-only nature, rate limits, or authentication needs, but it does describe the return structure (volume, value, top partners).

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 sized with a clear Args/Returns structure, though it could be slightly more concise.

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 output schema and annotations, the description provides adequate context for all parameters and return values, though it could mention data source or update frequency.

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 0% schema coverage, the description fully compensates by explaining each parameter with examples and defaults, adding significant 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 retrieves trade data between two countries for a product category, effectively distinguishing it from siblings that provide country-level stats or top partners.

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?

Usage is implied through parameter descriptions, but no explicit guidance on when to use this tool vs alternatives or exclusions 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?

No annotations are provided, so the description carries full burden. It discloses that the tool searches and returns a list, but lacks details on behavioral traits such as data freshness, rate limits, error handling, or pagination behavior. The description is minimal in this regard.

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 and well-structured: a single purpose sentence, followed by organized Args list, then Returns. Every sentence adds value. 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?

Given no output schema, the description mentions returns a list of suppliers with locations but lacks detail on the structure of each supplier entry. It also does not cover pagination or maximum limits. For a simple search tool, it is adequate but not fully comprehensive.

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 0%, so the description must compensate. It provides clear explanations for all parameters: country with examples, sector with examples and optionality, limit with meaning. This 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 searches for manufacturers/suppliers in a country via Open Supply Hub. The verb 'Sucht' (searches) and resource 'Fabrikanten/Lieferanten' are specific, and it distinguishes from sibling tools which focus on trade statistics and risks.

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 basic context (via Open Supply Hub) but does not explicitly state when to use this tool versus alternatives like get_country_trade_stats or get_supply_chain_risk. No when-not-to-use guidance is given.

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