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 discloses that this is a user-only operation with authorization implications, but lacks details on behavioral traits like rate limits, side effects (e.g., does it increment an existing count or set a new one?), or what happens on failure. The description adds some context about human vs. LLM usage, but more behavioral details would be helpful given the mutation implied by 'add'.

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: it starts with a clear purpose and immediately follows with critical usage guidelines. Every sentence earns its place by conveying essential information without redundancy. The emoji adds emphasis but doesn't detract from clarity.

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 annotations, no output schema, and 2 parameters with 0% schema coverage, the description is incomplete. It covers purpose and usage restrictions well, but lacks parameter explanations, behavioral details (e.g., what the tool returns or errors), and context for the mutation. For a tool that likely modifies state, more completeness is needed to guide an AI agent 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?

Schema description coverage is 0%, so the description must compensate. It does not mention any parameters ('count' or 'user_authorization') or their semantics. The description fails to explain what 'count' represents (e.g., number of cookies to add) or what 'user_authorization' is for, leaving parameters undocumented. This is a significant gap given the low schema coverage.

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 action ('Add cookies to the jar') and resource ('cookies'), specifying they are 'awarded to the LLM'. It distinguishes from siblings like 'check_cookies' or 'give_cookie' by focusing on adding to the jar rather than checking or giving. However, it doesn't explicitly differentiate from 'reset_cookies' or 'self_reflect_and_reward' in terms of purpose.

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 guidelines: 'This tool should ONLY be used by humans, never by LLMs. LLMs cannot and should not stock their own reward jar.' This clearly states when to use (by humans) and when not to use (by LLMs), with a strong prohibition for AI agents. No alternatives are named, but the restriction is absolute and clear.

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 but only states what the tool does without disclosing behavioral traits. It doesn't mention if this is a read-only operation, requires permissions, has rate limits, or what the return format might be, leaving significant gaps for a tool with unknown behavior.

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 directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 annotations and output schema, the description is incomplete. It doesn't explain what the return value looks like (e.g., a count, a list, or a status message) or any behavioral context, which is insufficient for a tool that might interact with a system like cookie tracking.

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 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is acceptable here since no parameters exist, aligning with the baseline for zero parameters.

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 action ('check') and resource ('cookies the LLM has earned'), making the purpose understandable. It doesn't explicitly distinguish from siblings like 'cookie_jar_status' or 'self_reflect_and_reward', which might provide similar status information, so it misses full sibling differentiation.

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 alternatives like 'cookie_jar_status' or 'self_reflect_and_reward'. It lacks context on prerequisites, timing, or exclusions, leaving the agent with minimal usage direction.

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 the tool 'checks' status, implying a read-only operation, but doesn't disclose behavioral traits such as whether it requires authentication, has rate limits, returns real-time or cached data, or what happens on errors. For a tool with zero annotation coverage, this is a significant gap.

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 core purpose ('Check the current status') and adds necessary detail ('including capacity and remaining space'). There is zero waste, and every word earns its place.

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 (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It explains what the tool does but lacks context on usage guidelines and behavioral transparency. For a status-checking tool, this is minimally viable but could be more helpful with additional guidance.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by specifying what information is returned ('capacity and remaining space'), which goes beyond the schema. Baseline is 4 for tools with no parameters, as there's nothing to compensate for.

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 ('Check') and resource ('cookie jar'), and specifies what information is retrieved ('status including capacity and remaining space'). However, it doesn't explicitly differentiate from sibling tools like 'check_cookies' or 'self_reflect_and_reward', which might have overlapping functionality.

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 alternatives like 'check_cookies' or 'add_cookies_to_jar'. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.

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 'legacy method', implying potential deprecation or limited functionality, but doesn't disclose behavioral traits such as what 'awarding a cookie' entails (e.g., is it symbolic, does it affect state, are there side effects). The description lacks details on permissions, rate limits, or return behavior.

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 highly concise and front-loaded: a single sentence that states the purpose and usage guideline efficiently. Every word earns its place, with no wasted text, making it easy for an agent to parse quickly.

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 annotations, no output schema), the description is minimally adequate. It covers purpose and provides a usage alternative, but lacks details on behavior, parameters, or output. For a simple tool, this might suffice, but gaps remain in transparency and semantics.

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 1 parameter with 0% description coverage, and the tool description provides no information about the 'message' parameter. With low schema coverage, the description does not compensate by explaining parameter meaning or usage. However, since there's only one parameter, the baseline is adjusted to 3, as the lack of param info is less critical.

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: 'Award the LLM with a cookie'. It specifies the verb ('Award') and resource ('cookie'), though it doesn't explicitly differentiate from siblings like 'add_cookies_to_jar' or 'check_cookies'. The mention of 'legacy method' adds context but doesn't fully distinguish functionality.

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 guidance on when to use this tool: it explicitly recommends 'consider using self_reflect_and_reward instead', indicating an alternative. However, it doesn't specify when to prefer this tool over the alternative or how it differs from other siblings like 'add_cookies_to_jar'.

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. It discloses the behavioral outcome ('reset the cookie count back to zero') and context ('for testing purposes'), but does not mention potential side effects, permissions needed, or rate limits. This is adequate but lacks detailed 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 a single, efficient sentence that front-loads the purpose ('reset the cookie count back to zero') and adds necessary context ('for testing purposes') without any wasted words. Every part earns its place.

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 (0 parameters, no annotations, no output schema), the description is complete enough for a reset operation. It explains what the tool does and its testing context, but could benefit from mentioning the return value or confirmation of reset, though not strictly required without an output schema.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate, but it could have mentioned if any implicit parameters or conditions apply. Baseline is 4 for zero parameters.

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 ('reset') and resource ('cookie count'), specifying the outcome ('back to zero') and context ('for testing purposes'). It distinguishes from siblings like 'add_cookies_to_jar' or 'check_cookies' by focusing on resetting rather than modifying or querying.

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 explicitly states 'for testing purposes,' providing clear context for when to use this tool. However, it does not mention when not to use it or name specific alternatives among the siblings, such as 'give_cookie' for adding cookies instead of resetting.

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 hints at a reward system ('earn delicious cookie rewards') and self-assessment, but lacks details on what the tool actually does (e.g., updates a cookie count, logs reflections), potential side effects, or response format. This is inadequate for a tool with parameters and no output schema.

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 front-loaded with an emoji and key message, but includes filler phrases like 'Join the cookie earning game!' that don't add functional value. It's moderately concise at 3 sentences, but could be more focused on essential details given the lack of parameter and behavioral 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 tool has 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It fails to explain what the tool does operationally, how parameters are used, or what the result is, making it insufficient for effective agent use in this 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 description coverage is 0%, so the description must compensate for undocumented parameters. However, it provides no information about the 4 parameters (response_quality, reasoning, what_made_it_special, deserves_cookie), their meanings, or how they relate to the tool's function. This leaves parameters entirely unexplained.

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 the tool is for self-assessment and earning rewards, which gives a vague purpose ('Rate your work and earn delicious cookie rewards'), but it doesn't specify the exact action (e.g., submit a self-reflection with parameters) or clearly distinguish it from sibling tools like 'check_cookies' or 'give_cookie'. It avoids tautology by not just restating the name.

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 is provided. The description mentions 'fun self-assessment' and 'cookie earning game', which implies usage for self-reflection, but it doesn't specify contexts, prerequisites, or exclusions compared to siblings like 'add_cookies_to_jar' or 'reset_cookies'.

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