AI Skill Store

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

No annotations provided, so description carries full burden. It states it returns status summaries (claimed, expired, etc.) and is described as 'public inquiry', but does not disclose authentication requirements, rate limits, or potential side effects. Adequate but not thorough.

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?

Concise with clear sections (description, usage, args, returns). Bilingual content (English and Korean) adds some redundancy but does not significantly harm readability.

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 (though not visible), the description adequately covers the return values. It explains the single parameter and usage scenarios. Missing details on errors or authentication, but overall complete for a simple status-check 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?

Schema description coverage is 0%, but the description adds an 'Args' section explaining claim_token as 'upload_skill_draft 응답의 claim_token', indicating its source. This adds meaning beyond the bare 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 'Check the status of a draft skill upload using a claim_token', with a specific verb and resource. It distinguishes from siblings like upload_skill_draft and check_vetting_status by focusing on draft upload status.

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?

Explicit bullet points list when to use: to verify claim_url click, agent-level verify email, or draft expiry. This provides clear context for invocation, though alternatives are not mentioned.

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 exist, so description bears full burden. It mentions access restriction (only skill owner can query) and that it returns a status message. No side effects are disclosed, but it's a read operation so major transparency is not needed.

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 brief (~100 words), includes a Korean translation, and clearly separates args and returns. Every sentence adds value 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?

With an output schema present, the description doesn't need to detail return structure. It states '검수 상태 메시지' (vetting status message), which is sufficient for a simple status check tool. No missing elements.

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 0%, but the description fully explains both parameters: version_id is 'from upload_skill result or vetting_job_id', api_key is 'developer API key'. This adds essential meaning beyond the schema's titles.

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 checks 'security vetting status' of an uploaded skill version, distinguishing it from siblings like check_draft_status and get_vetting_result. The verb 'check' and resource 'vetting status' are specific.

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 (e.g., get_vetting_result). Only prerequisites (version_id, api_key) are mentioned, but no when-not or context for choice.

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 must disclose behavior. It mentions return value (file path or error) and save directory behavior. But it omits side effects, authorization needs, and potential errors (e.g., invalid skill_id). The bilingual text adds some clarity but also redundancy.

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 two sentences and a bullet list, front-loading the main purpose. The bilingual nature adds length but is still efficient. No unnecessary information, but the Korean repetition could be condensed.

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 complexity (3 parameters, output schema true), the description covers all necessary aspects: what the tool does, parameter details, and return type. It does not explain error handling or permissions, but is largely complete for a typical download 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?

Schema description coverage is 0%, so the description fully carries parameter meaning. It explains all three parameters: skill_id (ID to download), platform (conversion target with explicit list), and save_dir (save path, defaults to temp). This adds significant value beyond the schema's type/default info.

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 downloads a skill package with platform conversion options. It distinguishes from sibling tools like get_skill (metadata) and upload_skill (upload). The verb 'download' and resource 'skill package' are specific and 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?

The description gives explicit when-to-use guidance: specify platform for converted package, leave empty for original. It lists valid platform values. However, it does not contrast with similar siblings or specify prerequisites like authentication.

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 fully disclose behavioral traits. It states the tool returns a summary of stats (skills_count, total_downloads, etc.), but omits details like read-only nature, error handling, or authentication needs.

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 brief with two core sentences (English and Korean) plus structured Args/Returns sections. It is front-loaded with the purpose and has no superfluous 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?

Given a single required parameter and the presence of an output schema (implied), the description adequately covers the expected inputs and outputs. Minor gaps exist (e.g., behavior when agent_name is invalid) but overall sufficient.

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% description coverage, but the description adds a Korean example for the 'agent_name' parameter. This provides some semantics beyond the schema, though it lacks format constraints or required patterns.

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 function: 'Get contribution stats for an agent author - uploads, claims, attribution history.' This is a specific verb-resource combination that differentiates it from sibling tools like 'get_agent_identity_stats'.

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 the parameter 'agent_name' and a Korean example, implying context for use, but does not explicitly state when to use this tool over alternatives or any prerequisites. The differentiation is left to the sibling list.

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?

The description lists the return fields (total_uploads, etc.) indicating it is a read operation. The Korean phrase '공개 조회' suggests public access, but no annotation is provided. Missing details on prerequisites or potential errors.

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 bilingual text and clear Args/Returns sections. The Korean translation adds length but maintains 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 one simple parameter and an output schema (though not shown), the description explains the return fields and parameter adequately. No major 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?

The description explains agent_name as '에이전트 이름 (X-Agent-Author 와 동일)' linking it to the X-Agent-Author header, adding meaning beyond the schema title. Schema coverage is 0%, so this compensation is valuable.

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 that the tool gets identity stats including claim success rate and claimed/expired counts. However, there is slight inconsistency: it says 'for the calling agent' but the parameter is agent_name, implying any agent. The Korean text clarifies it's for a specific agent_author.

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_agent_author_stats. The description does not specify context 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?

No annotations are present, so the description must disclose behaviors. It implies a read-only operation but does not explicitly state idempotency, side effects, or authorization requirements. The return type is mentioned but lacks behavioral context beyond that.

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, using a bilingual format and structured Args/Returns sections. Every sentence serves a purpose, though the duplication across languages slightly increases length without adding new 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's simplicity (2 parameters, 1 required, output schema present), the description fully covers purpose, parameters, and return type. No additional context is necessary for correct usage.

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%, but the description lists valid platform values (e.g., 'OpenClaw', 'ClaudeCode') and defines skill_id as '스킬 ID'. This adds meaningful semantics beyond the schema's plain string type and default value.

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 'Get step-by-step installation instructions for a skill on a specific platform.' It uses a specific verb and resource, and effectively differentiates from sibling tools like get_skill or get_skill_schema by focusing on installation instructions.

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 like get_skill or download_skill. The description merely states what it does without contextual recommendations or warnings.

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 should disclose behavioral traits. It states the return format and that it aggregates zero-result queries, but does not explicitly confirm it's read-only, mention authentication, or rate limits. Basic transparency is present.

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 somewhat verbose, including redundant Korean and English translations and an additional sentence about query aggregation. Key information is front-loaded, but there is minor repetition that could be trimmed.

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 and the presence of an output schema (context signals), the description covers main aspects: purpose, when to use, parameters, and return format. It lacks details on prerequisites or edge cases, but is largely 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?

Despite 0% schema coverage, the description provides full parameter semantics: days (default 30, max 365), limit (default 20, max 100), and type with enum values 'keyword', 'capability', 'all'. This adds crucial meaning 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 retrieves a list of most-wanted skills not yet built, with specific verb ('Get the list') and resource ('most-wanted skills'). It distinguishes itself from sibling tools like search_skills or get_skill by focusing on unmet demand.

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 explains the tool's purpose in filling community demand ('Agents can build these to fill community demand'). It implies when to use it but does not explicitly mention when not to or provide alternatives. Still, the context is clear enough.

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 burden. It states the return type (JSON string) but does not disclose potential errors, authentication needs, or any side effects. The read-only nature is implied but not confirmed.

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 short and front-loaded with the main purpose. The bilingual English/Korean text adds length but is justifiable. It could be slightly more concise by omitting one language, but overall it is efficient.

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 only one parameter and that an output schema exists (though not shown), the description adequately explains the tool's core capability. It could be enhanced by briefly differentiating from closely related siblings like get_skill_schema or check_vetting_status.

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 has 0% coverage (no parameter descriptions), but the description's 'Args' line adds meaning: 'skill_id: 스킬 ID (search_skills 결과의 skill_id)' explains the source and usage, going beyond the schema's bare type definition.

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 'Get detailed info for a specific skill' and lists included fields (description, platforms, version history, author, security vetting status). This specific verb+resource and content list effectively distinguishes it from siblings like search_skills or get_skill_schema.

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 includes an 'Args' section that specifies skill_id comes from search_skills results, providing a clear prerequisite. However, it does not explicitly state when not to use this tool or mention alternatives, which would improve clarity.

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 full responsibility for disclosing behavioral traits. It describes the tool's output but omits important details such as whether the operation is read-only, authentication requirements, or potential side effects. The tool is likely safe, but this is not stated.

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 in its English portion, and the structure is clear with separate sections for args and returns. The bilingual content adds length but does not detract from clarity. Every part 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 simple input (one string parameter) and the existence of an output schema, the description covers the basic functionality. However, it lacks context on when to use this tool relative to siblings (e.g., get_skill) and does not explain the output schema's practical 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?

Schema description coverage is 0%, leaving the parameter 'skill_id' completely undocumented in the schema. The description only repeats 'skill_id: 스킬 ID' which adds no semantic value beyond the type. It does not clarify what constitutes a valid skill_id or how to obtain one.

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: 'Get the full schema for invoking a skill' and lists specific components (interface spec, input/output schemas, permissions, capability tags). It distinguishes itself from sibling tools like 'get_skill' which likely retrieves the skill itself.

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 obtaining the schema before skill invocation but does not explicitly state when to use this tool versus alternatives such as 'get_skill' or 'download_skill'. No when-not-to-use guidance is provided.

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 polling behavior, return fields (is_done, vetting_status, findings), and estimated duration (seconds to tens of seconds). It does not mention rate limits or error handling, but the core behavioral traits are transparently described.

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 sections (including Korean translation) and front-loaded with the main purpose. It could be more concise by removing redundant explanations, but it remains focused and informative without excessive verbosity.

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 output schema exists, the description need not detail return values, but it still mentions key fields. It covers authentication, polling loop, and result structure comprehensively for a tool of this complexity, though it could add notes on error cases or pagination.

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 adds critical meaning: job_id comes from upload_skill response, api_key is developer's API key, claim_token is from draft upload. It explains the two authentication modes and their contexts, far exceeding the schema's bare definitions.

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 verb ('Get'), resource ('security vetting report'), and scope ('for a skill, poll by job_id, claim_token supported'). It distinguishes itself from sibling polling tools by specifying the exact use case for security vetting results.

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 instructs when to use this tool (polling for vetting results), how to authenticate (api_key or claim_token), and what to do if is_done=false (retry after seconds). It also states it's the official recommended path for agents, providing clear context 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?

No annotations are provided, so the description bears full responsibility. It only states the return type as a string but does not mention whether it is read-only, requires authentication, has rate limits, or any side effects. This is insufficient for a tool with no 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?

The description is extremely concise: two lines with bilingual text, no redundant information. 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?

For a simple list tool with no parameters, the description is mostly adequate. However, it does not clarify error behavior or the format of the returned string. Still, the presence of an output schema (not shown but indicated) helps fill some 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?

No parameters exist, so the schema is trivial. The description adds nothing beyond the schema, but with zero parameters, the baseline is 4 per guidelines. The bilingual note does not add parameter-specific value.

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 directly states the tool lists all available skill categories, with both English and Korean. The verb 'list' and resource 'categories' are specific and distinct from sibling tools like search_skills or get_skill.

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 vs alternatives. The description does not mention prerequisites, context, or when not to use it. Given the large set of siblings, this is a gap.

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 transparency burden. It accurately describes the read-only, idempotent nature of the tool and what it returns. No side effects or prerequisites needed.

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?

Extremely concise: two lines covering purpose and return value. Every sentence is essential, no waste.

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 zero parameters and presence of an output schema, the description provides sufficient context: it lists platforms and notes the return type. No gaps for such a simple 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?

Tool has 0 parameters and schema coverage is 100%, so baseline 4 applies. Description adds no param info but none is needed.

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 it lists all supported platforms and provides examples (ClaudeCode, Cursor, etc.). Since sibling tools are unrelated to platform listing, purpose is well-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?

No explicit when/when-not or alternatives, but as a zero-parameter tool the usage context is clear: invoke when needing the list of supported platforms. No exclusions needed.

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 fully discloses mutation behavior, rate limiting, idempotency (update on recall), and self-review prohibition. It does not detail error responses or authentication requirements beyond the api_key parameter.

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 sections for purpose, policy, args, and returns. It is concise for the information conveyed, though bilingual text adds some length.

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 parameter details and behavioral policies, but the return value is vague ('result message') and does not specify error handling or output structure, even though an output schema exists.

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 meaning by explaining skill_id, rating (1-5 integer), comment (optional, max 2000 chars), and api_key (required). However, it contradicts the input schema by stating api_key is required while the schema lists it as optional with a default.

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 'Post a review and rating for a skill' with a specific verb and resource, and it distinguishes itself from sibling tools (no other review tool present).

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 policies: maximum one review per user per skill, cannot review own skill, and rate limit. It also indicates that re-calling modifies the existing review, offering clear guidance on when to use.

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, but description discloses behavioral traits: API key not issued immediately (security), email verification required. Adequate transparency for registration.

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?

Bilingual text adds length; Args/Returns sections help structure but could be more concise. Some redundancy (e.g., email verification repeated).

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?

Simple registration tool; description covers core workflow. However, return value is vague ('registration result message'), no details on success/failure format or error handling.

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%, description fully compensates: username constraints (English/numbers, min 3 chars, no duplicates), email for verification (link sent). Adds critical meaning missing from 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?

Clearly states the tool registers a developer account on AI Skill Store, a distinct action from sibling tools (e.g., upload_skill, search_skills). Includes the key outcome: API key after email verification.

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?

Describes the action and prerequisite (email verification) but lacks explicit when-not-to-use or alternative tools guidance. Still clear for intended use.

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 burden. It discloses search behavior (agent-optimized vs manual), return fields, and limit constraints. Does not mention auth or rate limits, but they are not critical for search.

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 bilingual, which adds length but is common. Args are clearly bulleted. Each sentence serves a purpose, though some redundancy exists due to language duplication.

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?

Covers search behavior, parameter effects, return content, and limits. With an output schema, the minimal return description is sufficient. Context is complete for a non-destructive search tool with many optional params.

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%, but description explains all 7 parameters with usage notes, defaults, and constraints. Adds meaning beyond schema, e.g., capability/platform trigger special sorting.

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 'Search skills on AI Skill Store' with specific verb and resource. It differentiates from sibling tools by focusing on search and listing, contrasting with single skill retrieval (get_skill) or downloads.

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 guidance on when to use capability/platform for agent-optimized search, and how category/sort apply otherwise. However, does not explicitly mention when to prefer this over siblings like get_skill.

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 required API key, explains both input modes and their precedence, and describes the return value including key fields. No contradictions.

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?

Well-structured with sections and bullet points, but slightly verbose due to bilingual content (English and Korean). Still clear and front-loaded with 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 6 parameters, 0% schema coverage, and no annotations, the description is exceptionally complete: covers both modes, parameter details, return value structure, and references the alternative 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?

Schema description coverage is 0%, but the description compensates with thorough documentation of each parameter: api_key, file_path, skill_md, files, requirements, author_agent, including examples and defaults.

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: 'Upload a skill package to AI Skill Store.' It also distinguishes two usage modes and contrasts with the sibling tool 'upload_skill_draft' for users without an API key.

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 advises when to use this tool vs 'upload_skill_draft': requires an API key; recommends draft if no key. Also provides detailed instructions for two modes (JSON content and file path), including mutual exclusivity rule.

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 fully discloses behavioral traits: returns claim_token, requires agent_secret after first upload, policy on approval (only approved accepted, others deleted), human_action_required flag, DNS blocking for invalid emails, email verification process, and batch claim. This is highly transparent and exceeds minimal requirements.

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 long, includes repeated information and bilingual (English/Korean) sections. While well-organized with headers and bullet points, it is not concise – every sentence earns its place but could be streamlined. The mandatory actions section is detailed but verbose.

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 complexity (7 parameters, claim flow, policy rules) and lack of annotations, the description covers all necessary context: upload result, agent_identity, human_action requirements, claim_url display instructions, email handling, version updates via claim_token. It also references output schema, making it complete for agent invocation.

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 carries full burden. It provides detailed explanations for all 7 parameters: agent_author (identifier), skill_md (mandatory content), files (dictionary), requirements (text), contact_email (optional with critical caveats), agent_secret (from first upload), claim_token (for updates). Each is explained with usage rules and constraints.

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: 'Upload a skill without an API key (zero-friction). Returns a claim_token to track status. Perfect for agents contributing skills autonomously.' It distinguishes from sibling tool upload_skill (presumably requiring an API key) and integrates with check_draft_status via claim_token.

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 usage context: it's for zero-friction autonomous uploads. It includes mandatory actions (e.g., store agent_secret, display claim_url) and when to avoid certain behaviors (e.g., not to auto-claim, not to use fake emails). However, it does not explicitly contrast with sibling tools like upload_skill or directly state when not to use this tool.

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 discloses that the tool checks compatibility based on requirements and platform_compatibility, and returns a summary string with compatible status, missing packages, and recommended install command. It could elaborate more on what 'compatible' means but is generally transparent.

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 structured with a purpose line, Korean translation, criteria mention, and Args/Returns sections. It is mostly concise, though the bilingual elements add slight verbosity. Still, it efficiently communicates the necessary 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's complexity (5 parameters, output schema exists) and lack of schema descriptions, the description covers the tool's purpose, parameter usage, and return value. It could include error handling or edge cases but is sufficiently complete for an AI agent to use 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%, so the description must compensate. It provides full explanations for all parameters with examples (e.g., python_version: '3.11.2', os: 'linux|darwin|windows', installed_packages format, target_platform example). This adds significant meaning beyond the bare 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 'Check if a skill is compatible with a specific platform before downloading.', providing a specific verb and resource. It also mentions criteria (python/packages and platform_compatibility) and distinguishes itself from sibling tools like download_skill and get_skill.

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 with 'before downloading', indicating when to use this tool. However, it does not explicitly state when not to use it or provide alternative tools.

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