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

Annotations already include readOnlyHint=true, so the description's 'Query' is consistent. The DEMO note adds caution but no additional behavioral traits like rate limits or stability. Without annotations, description would need more; with annotations, it's adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is two sentences, front-loaded with the core purpose, and every phrase adds value. No redundant or filler content.

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 query tool with full schema and output schema present, the description covers purpose and data reliability. It could mention output format slightly, but overall complete enough for agent context.

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

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

Schema coverage is 100% with clear enums for commodity and market. The description does not add new meaning beyond what the schema provides (e.g., 'Kenya markets' aligns with market choices). Baseline score of 3 is appropriate.

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

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

The description states 'Query current commodity prices at Kenya markets', using a specific verb (Query) and resource (commodity prices). It distinguishes from siblings via the 'Western parallel' note and DEMO notice, clearly identifying its scope.

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

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

The description provides a DEMO warning and verification instructions, but does not explicitly guide when to use this tool over siblings like market_overview or price_trend_analysis. The 'Western parallel' offers context but not direct exclusion 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?

Annotations declare readOnlyHint=true, so safety is covered. The description adds 'DEMO data,' disclosing that results are not real, which is critical behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Three sentences, front-loaded with purpose, then use case, then demo note. No redundant information; every sentence adds value.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage, and demo nature. However, the ambiguity about multiple markets versus single market parameter leaves a gap. Given the simple tool and presence of output schema, it is moderately complete.

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

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

Schema coverage is 100% with clear descriptions for each parameter. However, the description claims 'multiple markets' while the schema only allows a single 'market' parameter, creating confusion. This mismatch undermines clarity.

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 'Query commodity prices for multiple markets and commodities in one call,' specifying a verb and resource. It distinguishes from siblings like commodity_price_query (single commodity) and regional_price_comparison (focused comparison) by emphasizing multi-market and multi-commodity scope.

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

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

The description says 'Useful for traders comparing portfolios across regions,' giving a clear use case. It implies breadth and efficiency but does not explicitly mention when to use alternatives or exclude cases.

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

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

Annotations already indicate readOnlyHint=true. The description adds 'DEMO historical simulation' and 'Western parallel: CME seasonal charts', which clarify the tool analyzes historical data rather than real-time. However, it does not disclose limitations like latency or data freshness beyond the simulation context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is concise with three sentences, each adding unique information: purpose, Western parallel for context, and simulation note. It is front-loaded with the core purpose.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and three well-described parameters, the description could be more complete by summarizing the output format (e.g., trend direction, forecast values) or clarifying that 'DEMO historical simulation' means no real-time data. It covers the tool's role but leaves gaps.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds no additional semantics beyond confirming the tool's purpose. Baseline of 3 is appropriate since the schema already provides necessary details.

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

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

The description states 'Analyse price trends for a commodity to inform sell/hold timing', which clearly identifies the action (analyse trends) and resource (commodity prices). It distinguishes itself from siblings like commodity_price_query (current price) and sell_hold_decision (decision after analysis).

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

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

No explicit guidance on when to use this tool versus alternatives like sell_hold_decision or regional_price_comparison. The description mentions 'inform sell/hold timing' but 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?

Annotations already declare readOnlyHint=true, so the description's indication of a read operation is consistent. The description adds important behavioral context: 'DEMO prices' warns that data is not real, and the reference to Western parallel tools provides operational insight. This goes beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is very concise: three sentences, each earning its place. The first sentence states the core action, the second provides a parallel to familiar tools, and the third gives a concrete use case. No redundant or unnecessary information.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (as indicated in context signals), the description does not need to detail return values. It covers purpose, use case, and behavioral caveat (DEMO). It could be slightly improved by mentioning the geographic scope (Kenya markets) more explicitly, but it is adequately complete.

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

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

Schema description coverage is 100%, with clear parameter names and descriptions. The tool description adds meaning by explaining that farmer_location provides 'transport cost context,' which clarifies why this parameter is needed and how it enriches the comparison.

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

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

The description clearly states the tool's purpose: compare commodity prices across all Kenya markets to find the best price. It provides a concrete use case ('should I take my maize to Nairobi or sell locally?'), which distinguishes it from sibling tools like commodity_price_query (which likely queries a single price) or sell_hold_decision (which focuses on timing).

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 gives a clear when-to-use example ('should I take my maize to Nairobi or sell locally?') and mentions a Western parallel (CME, DTN ProphetX), implying context. However, it lacks explicit when-not-to-use guidance or direct differentiation from sibling tools such as market_overview or price_trend_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?

Annotations already declare readOnlyHint: true. The description adds important behavioral context: it's a DEMO model and recommends verifying prices, indicating reliability limitations beyond the annotation.

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 three sentences, front-loads the purpose, and every sentence adds value (purpose, parallel, caveat). No wasted words.

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

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description adequately explains the tool's purpose and context ('harvest advisor'). It lacks explicit mention of the output type (recommendation) but the demo warning adds necessary caution. Sufficient for a read-only advisory 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?

Input schema has 100% description coverage for all parameters. The description does not add significant detail beyond the schema; it provides general context (e.g., storage cost in KES) but no syntax or format specifics. Baseline 3 is appropriate.

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

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

The description clearly states 'Give a sell/hold recommendation for a commodity based on price trends', specifying the verb and resource. It also differentiates from sibling tools (e.g., commodity_price_query) by framing it as a decision advisor for harvest management.

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

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

The description provides context ('what should I do with my harvest today?') and a caveat ('DEMO model', 'verify prices'), which guides appropriate use. However, it does not explicitly state when to use this tool versus alternatives like price_trend_analysis.

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