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

With no annotations provided, the description carries the full burden. It mentions 'one-shot batched distillation' implying batch processing but does not disclose whether the call modifies stored state, requires authorization, has rate limits, or what the return value is. The description is insufficient for behavioral understanding.

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: purpose then usage scope. It is front-loaded, efficient, and contains 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 fails to explain what happens after the call (e.g., storage, overwrite behavior) and does not describe the output format. Given the lack of output schema, the description should address return values or side effects. This is a significant gap for a tool that likely mutates state.

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 detailed property descriptions and enum. The description adds no parameter-specific insights beyond the schema. It reinforces the type of preferences but does not clarify how to format text or use subfields like useWhen/avoidWhen. 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 the tool performs one-shot batched distillation of concrete collaboration preferences, specifying the resource (interaction profile) and scope (response structure, explanation style, workflow, decision-making). It explicitly excludes personality labels, distinguishing it from sibling tools like distill_recent_style.

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 clear context on when to use the tool ('Use for response structure, explanation style, workflow, and decision-making preferences') and what to avoid ('not personality labels'). However, it does not explicitly name alternative tools or state when not to use it beyond the exclusion.

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 batched, one-shot, uses recent messages, treats as user-endorsed, and immediate activation. However, it lacks detail on side effects (e.g., whether it replaces existing habits) and the 'basic checks' criteria.

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?

Description is two sentences plus a brief usage note, no wasted words, and front-loads key information about purpose and constraints.

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 low complexity (1 parameter, no output schema, no nested objects), description covers purpose, usage, behavioral effects, and constraints. It lacks explanation of return values but that is acceptable without output schema. Could be more detailed on 'basic checks'.

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%, so baseline is 3. Description adds no additional meaning beyond the schema; it mentions '3–7 signature expressions' but schema already defines array constraints and field 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 uses specific verb 'distill' with resource 'recent user messages' and output 'signature expressions'. It clearly distinguishes from siblings like 'observe_user_message' by being a batched one-shot operation.

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 explicit usage guidance ('Use sparingly: at conversation seed-time, or when the agent feels its style brief is too thin'), but does not explicitly mention when not to use or list alternative sibling 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 should fully disclose behavior. It only states 'delete' without mentioning permanence, reversibility, or authentication needs. The destructive nature is implied but not elaborated.

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 short sentence that conveys the essential information without any fluff. Every word is necessary and well-placed.

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 simplicity of the tool (one param, no output schema), the description is minimal. However, it lacks context about success/failure indicators, whether the deletion is irreversible, or any side effects. More detail 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?

The schema already describes the parameter ('idOrText') with a clear description. The description redundantly says 'by id or exact text' but adds no new meaning. With 100% schema coverage, baseline is 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 specifies the action ('delete') and the resource ('collaboration preference'), and the distinction from sibling tools like 'forget_style_habit' is clear. The purpose is unambiguous.

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 alternatives such as 'pin_interaction_preference' or 'review_interaction_profile'. No when-not-to-use context is given.

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, and the description does not disclose behavioral traits such as idempotency, error handling (e.g., if habit not found), or side effects. As a delete operation, it is destructive, but this is not clarified.

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 sentence, front-loaded with the verb and resource. No unnecessary words; every part is essential.

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 delete tool, the core information is present. However, it lacks details on behavior when the habit does not exist, whether it is reversible, or any relationship with other tools like 'list_style_habits'. The absence of an output schema is noted.

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% for the single parameter 'idOrText'. The description adds no additional meaning beyond what the schema already provides (habit id or exact habit 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?

The description clearly states the verb 'Delete' and the resource 'style habit', and specifies the lookup method (by id or exact text). This distinguishes it from siblings like 'forget_interaction_preference' (different resource) and 'pin_style_habit' (different 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?

No explicit when-to-use or when-not-to-use guidance is provided. There is no mention of prerequisites or alternatives. The agent must infer usage context from the tool name and siblings.

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 the burden of transparency. It implies a read operation by 'return' and 'use lightly', but does not explicitly state that it is non-destructive or requires no authentication. The description is adequate but lacks explicit behavioral disclosure.

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 two-sentence structure with no redundant or wasted words. It is front-loaded and efficient, providing essential information without clutter.

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 tool is simple with one optional parameter and no output schema. The description explains when to call it and that it returns a 'short style brief', but does not elaborate on what the brief contains or how it should inform behavior. Given the lack of output schema, more detail on the return value 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 description coverage is 100%, with the single parameter 'context' well-described in the schema. The tool description adds no additional parameter semantics beyond what is already in the schema, so the 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 clearly states the verb 'Return' and the resource 'short style brief', with specific usage context ('at the start of a conversation or before drafting a friendly reply'). This distinguishes it from sibling tools that do other operations like distillation or memory scoring.

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?

It explicitly says when to call the tool ('at the start of a conversation or before drafting a friendly reply'), providing clear context. However, it does not mention when not to use it or offer alternatives among siblings, which would improve the score.

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 must disclose behavior. It lists evaluation criteria but does not specify return format, side effects (read-only assumed), or permissions. Some transparency added by listing dimensions, but vague on what 'score' entails.

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 sentence, front-loaded with action, no unnecessary words. Efficient and clear.

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, description partially covers context (dimensions scored) but lacks details on return value, usage context among siblings, and behavioral norms. Agent may not know what format the score takes or when to invoke this 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?

No parameters; schema coverage is 100% (empty). Description does not need to add parameter info, and baseline for 0 parameters is 4.

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 verb 'Score' and resource 'local style memory', listing specific dimensions (usable, stable, fresh, drift, over-imitation). Distinguishes from siblings like get_style_brief and get_style_memory_status by focusing on scoring health aspects.

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 vs siblings like get_style_brief or review_style_habits. The description implies a health-check use case but does not state when it is appropriate or when alternatives are better.

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 describes the output but does not disclose behavioral traits such as read-only nature, file system access, or any side effects. The description is adequate but minimal.

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?

One succinct sentence that is front-loaded with the key action and resource. 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?

Given no parameters and no output schema, the description fully captures what the tool does: it shows the location and count. This seems complete for a simple status read.

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 no parameters with 100% coverage. The description adds value by explaining what the tool does and what it returns, which is beyond the empty 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 shows the local JSON store location and habit count, with a specific verb and resource. It distinguishes from sibling tools like list_style_habits or get_style_memory_score.

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 implies usage for checking memory status, but does not explicitly state when to use this tool versus alternatives like get_style_memory_score or list_style_habits.

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 the full burden. Description only states it lists preferences, implying a read operation but lacks disclosure of side effects, performance, memory usage, or any behavioral traits beyond the basic action.

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 sentence, no wasted words. Front-loaded with verb and resource. Succinct and clear.

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 list tool with no parameters and no output schema, the description is adequate but could be more complete by specifying the returned data structure or fields. It mentions the content type but not the format.

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?

No parameters defined. Parameter count is 0, so baseline is 4. Description does not need to add parameter info, and it does not provide any, which 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?

Description clearly states the tool lists stored preferences from a specific store. Verb 'List' and resource 'collaboration and response-structure preferences' are specific. Distinguishes from siblings like 'get' or 'distill' tools, though similar list tools like 'list_style_habits' exist, but the resource is distinct.

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_style_brief' or 'list_style_habits'. No exclusions or context provided 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?

With no annotations, the description carries full burden. It mentions the source ('local JSON store') and content ('stored style habits and candidates'), indicating a read operation, but lacks details on side effects, cost, or 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?

The description is a single sentence of 15 words, highly efficient and front-loaded. Every word adds value with no fluff.

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 list tool with no parameters and no output schema, the description is mostly complete. It could mention if there is any pagination or filtering, but the context signals (device complexity) suggest this is minimal.

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?

There are zero parameters, and schema description coverage is 100% (empty schema). The description adds value by clarifying that both 'stored style habits and candidates' are listed, which is beyond the schema. Baseline for zero params is 4.

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 name 'list_style_habits', title 'List style habits', and description 'List stored style habits and candidates from the local JSON store' clearly specify the action (list), resource (style habits and candidates), and source (local JSON store). It is well-differentiated from siblings like 'list_interaction_profile'.

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 vs alternatives like 'review_style_habits'. The description implies listing, but does not state when not to use it or contrast with siblings.

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 behavioral disclosure. It states 'Learn' which implies a side effect (updating style memory), but it does not explicitly confirm persistence, return behavior, or whether it is idempotent. This leaves ambiguity for the agent.

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 long, front-loaded with the core purpose, and contains no extraneous information. Every phrase earns its place, making it efficient and easy to parse.

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 lack of output schema and no annotations, the description could be more complete by explicitly stating whether the tool stores observations or returns a result. The mention of 'three repetitions' for stable habits hints at storage, but it is not confirmed, leaving a gap in 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 100%, but the description adds valuable context beyond what the schema provides: emphasizing security (not to pass secrets) and explaining the purpose of hints with examples (e.g., 'self-invented sentence-final particle'). This clarifies parameter usage meaningfully.

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: learning lightweight conversational style signals from the latest user message. It uses a specific verb 'Learn' and resource 'conversational style signals', and conceptually distinguishes itself from sibling tools focused on retrieval, forgetting, or pinning.

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 guidance on what to pass (only message text, not secrets or logs) and hints about optional usage. However, it does not explicitly contrast when to use this tool versus alternatives like distill_recent_style or review_style_habits, limiting its utility for tool 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 full burden. It discloses that the tool toggles pin status to prevent deletion, but omits details like idempotency, permission requirements, or side effects, leaving significant 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?

A single, front-loaded sentence of 12 words that efficiently conveys the purpose and outcome with no wasted 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?

For a simple toggle tool with two parameters and no output schema, the description covers the core functionality. However, it lacks details on return behavior or confirmation messages, which would be helpful for 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 100% and both parameters have descriptions in the schema. The description adds no additional meaning beyond what the schema already provides, meeting the baseline but not exceeding it.

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 verb ('Pin or unpin') and the resource ('collaboration preference'), with the outcome ('so cleanup will not delete it'). However, it does not explicitly distinguish from sibling tools like 'pin_style_habit', though the title differentiates by resource.

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 implies usage context (cleanup avoidance) but provides no guidance on when to use this tool versus alternatives like 'pin_style_habit' or when not to use it.

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 for behavioral disclosure. It only states that pinning protects from cleanup (deletion), but omits important details like whether unpinning also reverses protection, permission requirements, side effects on other operations, or idempotency of repeated calls.

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 sentence with no wasted words. It is front-loaded with the actionable verb and resource, immediately clarifying the tool's function. Every element serves a 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 tool's simplicity (2 params, no output schema, no annotations), the description is minimal but incomplete. It lacks details on return values, error handling, matching semantics for idOrText (exact match, case-sensitivity), and whether the operation is idempotent. An agent using this tool would be uncertain about behavior in edge cases.

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% (both parameters have descriptions in the schema). The tool description adds minimal value beyond the schema, merely connecting the parameters to the purpose. Per guidelines, baseline 3 is appropriate when schema already provides sufficient semantic meaning.

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 verb 'Pin or unpin' and the resource 'style habit', with the explicit purpose 'so cleanup will not delete it.' It effectively distinguishes from sibling tools like pin_interaction_preference by specifying the target resource.

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 alternatives such as pin_interaction_preference. There is no mention of when not to use it or any prerequisites. The context implies usage for habit pinning, but lacks explicit actionable guidance.

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?

Discloses return of a review queue and suggested actions, but does not specify read-only nature, response format, or any side effects. With no annotations, more detail would improve transparency.

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 sentence directly states purpose and key output without any 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?

Adequate for a simple tool with one parameter, covering the main functionality and output, though lacks detail on response structure and potential constraints.

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 no parameter information beyond what the schema provides; since schema coverage is 100% and the limit parameter is well-described there, this is adequate.

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 the tool returns a review queue for stored collaboration preferences with suggested actions, distinguishing it from sibling tools like list_interaction_profile and review_style_habits by specifying content and actions.

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 such as list_interaction_profile or forget_interaction_preference; usage context is only implied.

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 states the tool 'returns a concise review queue with suggested actions,' but does not disclose whether this is a read-only operation, whether it modifies any state, or how the suggestions are generated. The behavioral implications are unclear.

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 purpose. Every word earns its place without 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 has no output schema and only one parameter, the description is incomplete. It does not explain what the review queue contains (e.g., habit details, current state, rationale for suggestions) or how the suggested actions are meant to be used. More detail is needed for effective use.

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 only parameter, 'limit', has 100% schema description coverage. The description adds 'concise review queue,' which hints at the limit controlling conciseness, but does not add meaningful information beyond the schema. Baseline 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 clearly states the tool returns a 'review queue' with suggested actions like keep, pin, forget, or observe. It specifies the resource (style habits) and action (review). It distinguishes from sibling tools such as list_style_habits (which likely only lists) and the action-specific tools (pin_style_habit, etc.).

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 implies usage for reviewing habits and deciding actions, but does not explicitly state when to use this tool versus alternatives like list_style_habits or the individual action tools. No exclusions or prerequisites are mentioned, leaving some ambiguity.

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. It mentions modifying a 'local JSON store' but does not disclose behavioral traits such as persistence, side effects on existing learned styles, or any required permissions. The behavioral context is minimal.

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?

A single, concise sentence that efficiently communicates the tool's purpose with no redundant 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 simple tool with one boolean parameter and no output schema, the description provides the core function. However, it lacks details on behavioral implications (e.g., impact on existing data, whether changes are reversible). Completeness is adequate but not exceptional.

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% and the parameter description is already clear. The tool description does not add new meaning beyond repeating what the schema says ('Set false to stop learning new style signals'). Baseline score applies.

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 verb 'Enable or disable' and the resource 'style learning in the local JSON store'. It distinguishes from sibling tools like 'observe_user_message' and 'distill_interaction_profile' by focusing specifically on toggling the learning setting.

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 implies usage for enabling or disabling learning but does not provide explicit guidance on when to use this tool versus alternatives like 'observe_user_message' or 'distill_interaction_profile'. No exclusions or when-not-to-use scenarios are mentioned.

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