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 effectively describes key traits: it's a read operation (implied by 'returns'), specifies ranking criteria ('ranked by 24-hour volume (desc)'), and details the return format (JSON array with schema). However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some behavioral aspects uncovered.

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 and front-loaded: the first sentence states the purpose and usage context, followed by a structured 'Parameters' and 'Returns' section. Every sentence earns its place by providing critical information without redundancy, making it efficient and well-organized.

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

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

Given the tool's moderate complexity (2 parameters, no annotations, no output schema), the description is complete enough. It covers purpose, usage guidelines, parameter semantics, and detailed return format (including schema and parsing instructions). The absence of an output schema is compensated by the thorough 'Returns' section, ensuring the agent can understand and use the tool effectively.

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?

Given 0% schema description coverage, the description fully compensates by explaining both parameters: 'topic_slug' is described as an 'Identifier such as "trump-presidency"', and 'limit' as 'Number of markets to return, ranked by 24-hour volume (desc)' with a default value. This adds essential meaning beyond the bare schema, making the parameters clear and actionable.

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 ('Call after you have a Polymarket topic slug... and need the top markets under it') and distinguishes it from sibling tools by mentioning when to use it versus 'prediction_markets_trending_topics' for obtaining the slug. It specifies the verb ('need the top markets') and resource ('markets under [a topic]'), making the purpose explicit and differentiated.

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 explicit guidance on when to use this tool: 'Call after you have a Polymarket topic slug (from the user or `prediction_markets_trending_topics`)'. It distinguishes from the sibling 'prediction_markets_trending_topics' by indicating that tool as a source for the slug, and implicitly contrasts with 'search_prediction_markets' by focusing on top markets by topic rather than a general search.

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 explains the tool's behavior: retrieving trending topics ordered by 24-hour volume and returning a JSON array string. However, it doesn't mention rate limits, authentication requirements, data freshness, or error conditions. The description adds some value but lacks comprehensive behavioral 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 well-structured and appropriately sized. It begins with usage guidance, states the core functionality, explains the sibling tool relationship, and clearly documents the return format. Every sentence earns its place with no wasted words. The Returns section is properly formatted and informative.

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

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

Given the tool's simplicity (zero parameters, no output schema, no annotations), the description provides adequate context. It explains what the tool does, when to use it, how to use the output with sibling tools, and the return format. For a straightforward read-only tool, this is reasonably complete, though it could benefit from mentioning data source limitations 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?

The tool has zero parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist. It does mention the output format, which is helpful context. Baseline for zero parameters is 4, and the description meets this standard.

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: retrieving today's trending Polymarket topics ordered by 24-hour volume. It specifies the verb ('retrieves'), resource ('trending Polymarket topics'), and ordering criterion. However, it doesn't explicitly differentiate from sibling tools like 'search_prediction_markets' beyond mentioning one specific follow-up 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?

The description provides explicit usage guidance: 'Use when analysing a user's portfolio and you need external sentiment from prediction markets.' It also specifies when to use a sibling tool: 'Pass any slug from this list to `prediction_markets_markets_by_topic` to pull the individual markets for that theme.' This clearly defines the context and alternative workflow.

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 clearly describes the search functionality and output format, including the JSON structure and parsing instruction. However, it lacks details on rate limits, authentication needs, error handling, or whether this is a read-only operation (implied by 'Search' but not explicit). The description adds useful context but misses some behavioral traits.

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 well-structured with clear sections (purpose, parameters, returns) and front-loaded key information. Every sentence adds value, such as the parsing instruction. It's appropriately sized for a single-parameter tool, though the returns section is detailed, which is necessary given the lack of output schema.

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 output schema, no annotations), the description is fairly complete. It covers the purpose, parameter semantics, and output format in detail, which is crucial since there's no output schema. However, it lacks sibling differentiation and some behavioral context, which slightly reduces completeness for an agent's decision-making.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'search_term' is an 'Identifier such as "argentina"', providing a concrete example and clarifying it's a string for searching. This compensates well for the schema's lack of documentation, though it could elaborate on format constraints or examples.

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 with a specific verb ('Search') and resource ('prediction markets'), and specifies the search scope ('by any term'). However, it doesn't explicitly differentiate from sibling tools like 'prediction_markets_markets_by_topic' or 'prediction_markets_trending_topics', which likely have different search approaches or criteria.

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 its siblings. It mentions searching 'by any term' but doesn't clarify if this is for general keyword searches, while siblings might filter by topic or trending status. There are no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer usage from tool names alone.

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