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

With no annotations, the description carries the full behavioral burden. It discloses result-quality expectations and an exclusion, but omits operational details such as side effects, determinism with seed/count, required permissions, 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?

Three short sentences, front-loaded with the core action and immediately followed by usage and exclusion criteria. Every sentence contributes without repetition or filler.

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

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

An output schema exists, so return values need not be described. But with no annotations and 8 parameters at 0% schema-description coverage, the description is not complete enough for an agent to invoke all optional parameters confidently.

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% across 8 parameters. The description only implicitly references the two required concepts and adds no meaning for seed, count, language, constraints, include_trace, or parallel_chains, leaving most parameters undocumented by either schema or description.

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

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

States a specific verb-resource pair: 'Cross two concepts into ideas.' It clearly explains the recombination purpose but does not name or distinguish itself from its siblings creative_generate, creative_expand, or creative_mutate.

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 a clear use case ('Use for non-obvious recombination') and an exclusion ('not a factual reasoning or final-selection tool'). However, it does not explicitly name alternative tools or explain when to choose those over this one.

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, the description carries the full behavioral burden. It adds useful context that the core concept is preserved and that this is for open-ended ideation rather than lookup or final selection, but it does not disclose operational traits such as non-determinism, the effect of seed/count/parallel_chains, or any permissions 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?

Two short sentences, front-loaded with the core action and followed by usage boundaries. Every sentence adds useful selection context 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?

The description is inadequate for an 8-parameter tool with no annotation or schema-description support. The output schema covers return values, but an agent still lacks the parameter guidance needed to invoke the tool correctly.

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

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

Schema description coverage is 0% and the tool has 8 parameters. The description only implies the required 'idea' parameter via 'starting concept'; it says nothing about goal, seed, count, language, constraints, include_trace, or parallel_chains.

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 a specific verb ('Expand') and resource ('a starting concept') with the intended output types: interpretations, mechanisms, or embodiments. It distinguishes the tool from factual lookup and final-answer selection, but does not name or differentiate itself from sibling tools like creative_generate or creative_mutate.

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

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

Explicitly says to use it for open-ended ideation and to explore a concept's possibility space without replacing the core concept. It also rules out factual lookup and choosing a final answer. However, it does not route the agent among the sibling creative tools.

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

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses almost nothing operational: no determinism guarantees, no cost/latency or rate behavior, no indication of how many directions are returned or how 'parallel_chains'/'seed' affect output. It communicates the generative, non-factual nature of the tool, but for a 9-parameter generative tool that is thin.

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 the core action before the usage and exclusion lists. The brainstorming/exploration list is somewhat padded with overlapping items, but overall it is tight and easy to scan.

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?

An output schema exists, so return values need not be described, and the usage framing is complete. What is missing is coverage of the 9 inputs and any behavioral context, leaving the agent able to decide when to call it but not how to configure it.

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% across 9 parameters, so the description must compensate and largely does not. The phrase 'multi-step RD' loosely hints at the method enum's 'rd_full' default, but seed, count, context, constraints, language, include_trace, and parallel_chains receive no explanation at all.

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 a specific verb ('Generate') and resource ('conceptually diverse, unconventional candidate directions') plus a technique ('multi-step RD'), so the core operation is unambiguous. However, with three closely related siblings (creative_expand, creative_mutate, creative_cross), the description never distinguishes what makes this one different, which is the main clarity gap.

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?

Strong explicit usage list ('open-ended exploration, brainstorming, alternative designs, research directions, creative problem solving, search-space expansion') followed by a clear exclusion list ('not for factual lookup, exact answers, calculations, or selecting the final best answer'). It stops short of naming which sibling to use instead, so it lacks the routing detail a 5 would require.

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 bears the full disclosure burden. It conveys mutation semantics (substantial, non-cosmetic change with preserved properties) and negative scope, which is valuable, but is silent on determinism via seed, batch behavior (count, parallel_chains), trace output, and language handling — all relevant for an 8-parameter generation tool.

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 short sentences, front-loaded with the core behavior and followed by the usage condition. Efficient, though the closing exclusion could arguably be folded into the usage sentence.

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?

An output schema exists so return values needn't be described, but with 8 parameters at 0% coverage and no annotations, the definition leaves critical operational parameters unexplained. An agent could not predict the effect of count, parallel_chains, seed, or include_trace from this description.

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% across 8 parameters, so the description must compensate and largely does not. Its phrase 'retaining explicitly requested properties' hints at the 'preserve' parameter, but seed, count, language, constraints, include_trace, and parallel_chains are left entirely undefined.

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 a specific verb and resource: 'create substantially different variants while retaining explicitly requested properties.' It distinguishes itself from 'cosmetic rewrites,' which is useful, but never differentiates from the sibling tools creative_generate/expand/cross, so an agent cannot tell which variant-producing tool to pick from purpose alone.

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?

Gives a clear when-to-use condition ('an existing proposal should change in mechanism or expression') plus an explicit exclusion ('not intended to rank or select variants'). Missing is any reference to the alternatives (creative_expand, creative_cross) that compete for this use case.

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