AI Skill Store

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

No annotations are provided, so the description must disclose behavioral traits. It states the return status summary values (claimed, expired, etc.), implying a read-only operation. However, it does not explicitly confirm non-destructive behavior or mention any prerequisites, but overall it is adequate.

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 and well-structured: purpose statement, usage points in bullet-style, args section, and returns. Every sentence adds value without unnecessary 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 only one parameter and no annotations, the description covers the necessary context: what the tool does, when to use it, the parameter's source, and the return values. The presence of an output schema (though not shown) further reduces the need for detailed return descriptions.

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?

With 0% schema description coverage, the description adds meaning by explaining that claim_token is the response from upload_skill_draft. This provides context beyond the schema's mere type declaration, helping the agent understand the parameter's origin and purpose.

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,' which is a specific verb+resource. It distinguishes itself from sibling tools like check_vetting_status and upload_skill_draft by focusing on draft status checking.

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 usage scenarios are provided: checking if user clicked claim_url, if verify email was processed, or if draft is claimed/expired. It also specifies that claim_token comes from upload_skill_draft response, offering clear when-to-use 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?

With no annotations, the description carries the burden. It discloses that the API key is needed and only the skill owner can access, implying authorization. It also indicates a read-only check by nature. However, it could mention that it is non-destructive or provide rate limit info.

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 bilingual but concise, with a clear purpose statement, context reminder, and structured parameter list. It is front-loaded with the key action. Slightly less concise due to dual language, but appropriate for the audience.

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 output schema exists (not shown) but the description only says 'Returns: 검수 상태 메시지' without any details. For a tool with 2 required params and an output schema, the description should summarize the response structure or fields to guide 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 has 0% description coverage (only titles), but the tool description includes an Args section with meaningful explanations: version_id is from upload_skill and can be vetting_job_id, api_key is developer key with owner-only access. This adds value beyond the 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 verb 'Check' and the resource 'security vetting status of an uploaded skill version'. It also specifies required inputs (version_id, api_key). This distinguishes it from siblings like get_vetting_result or check_draft_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?

The description implies usage after upload_skill by mentioning version_id from that tool, and notes that only the skill owner can query. However, it does not explicitly state when to use this tool vs alternatives (e.g., get_vetting_result) or provide when-not-to-use scenarios.

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 key behaviors: the tool downloads a skill package, can auto-convert for platforms, saves to a specified directory or temp, and returns a file path or error. It does not explicitly state it is a read-only operation, but the action 'download' strongly implies no side effects on the server. This is adequate considering the tool's simplicity.

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 a short introductory sentence followed by parameter explanations and a return value note. It includes both English and Korean, which adds length but may be necessary for the user base. It is front-loaded with the core purpose. A slightly more concise version could merge the two languages, but currently it does not waste 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 the presence of an output schema, the description need not detail return values, but it does so anyway ('stored file path or error message'). It covers parameters, usage, and output. However, it does not mention prerequisites (e.g., valid skill_id, published status, authentication) or potential errors (e.g., invalid platform). For a tool with 3 parameters and no annotations, this is mostly complete but could be improved.

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 compensates. The 'Args' section explains each parameter: 'skill_id' (required), 'platform' (with default and explanation of behavior when empty), and 'save_dir' (with default and storage behavior). This provides complete semantic meaning beyond the schema's property names and types.

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: 'Download a skill package.' It specifies the verb 'download' and the resource 'skill package', and distinguishes from siblings like 'get_skill' (retrieves metadata) and 'upload_skill' by focusing on file retrieval. The additional detail about auto-conversion for platforms further clarifies the tool's specific 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 guidance on when to use the 'platform' parameter and what happens when left empty ('original .skill download'). It lists example platforms. However, it does not explicitly contrast with sibling tools or state when not to use this tool, missing an opportunity to guide selection among alternatives like 'get_skill' or 'get_install_guide'.

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 that it returns skills_count, total_downloads, etc. as a summary. No annotations provided, so description bears full burden; it adequately describes the output but doesn't mention any side effects or caching behavior. Still, for a stats retrieval tool, this is transparent enough.

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 brief, front-loaded with English and Korean, and includes a list of return fields. No unnecessary words, but could be slightly more structured.

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 one-parameter stats tool with an output schema, the description covers the function and return values adequately. However, it lacks details on error conditions or rate limits, but given the simplicity, it's sufficiently 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?

Schema has 0% description coverage, but description adds a Korean explanation and example for the single parameter 'agent_name', which provides crucial context beyond the 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 verb 'Get' and resource 'contribution stats for an agent author', with specific examples of stats. Distinguishes from siblings 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?

No explicit guidance on when to use this tool vs alternatives. Only describes what it does, implying usage for retrieving author stats, but no 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 provided, so the description must fully disclose behavioral traits. It states the tool is a public inquiry and lists return fields, but it does not mention whether it is read-only, authentication requirements, side effects, or error handling if the agent_name does not exist.

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, bilingual, and well-structured with labeled Args and Returns sections. 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?

Given the tool's simplicity (one parameter, output schema described), the description is largely complete. The only notable gap is not clarifying whether the agent_name must match the caller's identity, which could cause confusion.

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% description coverage, but the tool description adds meaning to the single parameter agent_name by stating it is the same as the X-Agent-Author header and provides a Korean explanation. This compensates well for the lack of schema documentation.

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 identity stats (claim success rate, claimed/expired counts) for the calling agent or a specified agent_author. However, there is minor ambiguity: it mentions both 'calling agent' and a parameter 'agent_name', leaving unclear whether it always returns stats for the caller or for any agent.

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 explicit guidance on when to use this tool versus alternatives like get_agent_author_stats. It does not mention prerequisites, context, 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?

No annotations provided; description indicates a simple read operation returning a string, but does not detail side effects, permissions, or other behavioral traits beyond the basic purpose.

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 docstring format with Args and Returns sections, but includes bilingual text which may slightly reduce clarity for non-Korean readers.

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 parameters and the existence of an output schema, the description sufficiently covers the tool's functionality and return value.

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 beyond the schema by listing parameters with explanations and enumerating possible values for platform, compensating for the 0% schema description 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 tool returns step-by-step installation instructions for a skill on a specific platform, distinguishing it from sibling tools like get_skill 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 implies usage for obtaining installation instructions but lacks explicit guidance on when to use this tool versus alternatives, or any prerequisites.

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 only partly compensates by noting the source (aggregated zero-result search queries) and that uploads will have demand. However, it does not explicitly state the tool is read-only or mention side effects, which is acceptable for a get tool but leaves room for improvement.

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 clear sections (purpose, args, returns) and is front-loaded with the key purpose. However, there is redundancy between the English and Korean text, and the length could be slightly 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?

For a simple tool with 3 optional parameters and an output schema (though not shown), the description explains the return format and each parameter. It lacks error conditions or prerequisites, but overall it is sufficient for an 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?

The input schema has 0% description coverage, but the description's 'Args' section provides explanations, defaults, and ranges for days, limit, and type. The explanations are in Korean, which may limit usability for English-only agents, but they add substantial value 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 'Get the list of most-wanted skills that haven't been built yet,' specifying the verb and resource. The concept of 'Supply Loop' and 'community demand' distinguishes it from sibling tools like get_skill or search_skills, but the mixed English and Korean language and lack of explicit distinction wording slightly reduce clarity.

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 ('Agents can build these to fill community demand') but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance. No comparison with sibling tools 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 provided. Description adds return format details but omits behavioral traits like idempotency, error handling, or auth requirements. Acceptable for a simple read operation but insufficient for full 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?

Two concise sentences in English plus Korean translation; well-structured. Korean section adds value but would benefit from including args/returns detail in English as well.

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 an output schema exists (not shown but indicated), description adequately covers expected return values. Could mention possible errors (e.g., skill not found) but not critical for this 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?

Schema has 0% coverage; description compensates by explaining skill_id as 'skill ID from search_skills result', adding meaningful context beyond the schema's 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?

Description clearly states 'Get detailed info for a specific skill' and lists included fields. Distinguishes from siblings like search_skills (list) and get_skill_schema (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?

Implies usage after search_skills by referencing its output, but no explicit when-to-use or alternatives. Lacks guidance on when not 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It only states what the tool returns, not whether it is read-only, requires permissions, or has side effects. For a simple get operation, this is insufficient detail.

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 includes bilingual text, but the 'Args' and 'Returns' sections add minimal value as they only restate the schema. It is front-loaded with the main purpose, but some repetition could be removed.

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 explains what the output contains, which is sufficient given an output schema exists. However, it lacks usage guidance and parameter details. For a simple tool with one parameter, it is adequate but not rich.

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, and the description's 'Args' section merely repeats the parameter name in Korean ('스킬 ID'). No additional meaning is added about the format, source, or constraints of skill_id.

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 the full schema for invoking a skill' and lists exact contents (interface spec, input/output schemas, permissions, capability tags). This distinguishes it from sibling tools like 'get_skill' which retrieves the skill itself, not the 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 implies usage: to retrieve a skill's schema before invocation. However, it does not explicitly state when not to use it or mention alternatives. The sibling list suggests other tools exist for different purposes, but no direct guidance 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?

With no annotations, the description carries the full burden. It discloses polling behavior, the is_done flag, expected wait times, and authentication restrictions. It does not mention rate limits or destructive potential, but provides sufficient behavioral context for an 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 well-structured with sections but includes redundant bilingual content (Korean and English). While front-loaded with the purpose, the length could be trimmed without losing meaning.

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 provided in detail), the description covers polling behavior, authentication, and return fields. It is sufficiently complete for an agent to use correctly, but could mention pagination or 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 coverage is 0%, so the description must fully compensate. It explains each parameter's origin (job_id from upload responses, api_key as uploader's key, claim_token from draft upload) and their interdependencies, enabling correct parameter selection.

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 it retrieves a detailed security vetting report for a skill, using a polling mechanism with job_id. It distinguishes itself from siblings like check_vetting_status by being the 'official recommended path' and mentioning claim_token support.

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 specifies when to use (after uploading a skill) and provides authentication options (api_key vs claim_token). It implicitly suggests this is the primary tool for polling results, but does not explicitly contrast with sibling tools like check_vetting_status.

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 behavior. It states 'List all available skill categories,' but the return description '카테고리 목록 문자열' is ambiguous—it does not clarify if the output is a list of strings or a single string. For a read-only operation, more clarity on the return format is 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 concise with two short sentences and a returns line, but the inclusion of a Korean duplicate adds slight redundancy. It is front-loaded with the key action.

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 there are no parameters and an output schema exists, the description is mostly complete for a simple list tool. However, it lacks context about typical use (e.g., for populating dropdowns) and does not specify if categories have IDs or order.

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, so the description adds no parameter information beyond the schema. With 100% schema coverage and no parameters, the baseline of 4 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 it lists all available skill categories, using a specific verb and resource. It distinguishes from siblings like 'list_platforms' and 'search_skills' because it focuses solely on categories.

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 does not provide any guidance on when to use this tool versus alternatives, such as when searching for specific categories or filtering. Usage is implied by the simplicity of the tool, but explicit context is missing.

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 notes the return is a 'platform list string' but lacks details on format (e.g., comma-separated, newline) or any constraints. With an output schema present, this is minimally acceptable but could be more informative.

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 two short sentences and a returns clause. The Korean translation adds minor redundancy but does not significantly detract. Every sentence 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?

For a simple list tool with no parameters and a known output schema, the description is largely complete. It states the output is a string representing platform list, which is sufficient given the schema provides structure.

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 no parameters (0), and schema description coverage is 100% by default. The description does not need to add parameter info, meeting the baseline of 4 for no-parameter tools.

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 explicitly states 'List all supported platforms' with concrete examples (ClaudeCode, Cursor, etc.), making the purpose highly clear. It distinguishes from sibling tools like list_categories which list categories, not platforms.

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 clearly indicates when to use the tool (when needing a list of supported platforms), but does not mention any alternatives or when-not-to-use scenarios. However, no sibling tool overlaps in function, so guidance is adequate.

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 discloses mutation (re-call modifies existing review), prohibition on self-review, and rate limiting. This provides comprehensive behavioral context beyond what structured fields offer.

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, policies, args, and returns. It includes bilingual text which may add length, but information is front-loaded and each sentence adds value. Could be slightly more concise but not overly 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 no annotations, the description covers purpose, constraints, and all parameters. Return value is vaguely stated as '결과 메시지' but output schema exists to clarify. The description is adequate for a simple write operation, though error handling is not mentioned.

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%, meaning the schema provides no descriptions. The description compensates fully by explaining each parameter: skill_id (skill to review), rating (integer 1-5), comment (optional, max 2000 chars), api_key (required). This adds substantial meaning beyond the 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 'Post a review and rating for a skill.' It uses a specific verb and resource, and distinguishes itself from sibling tools (none of which are review-related).

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 constraints: max one review per user-skill, cannot review own skill, rate limit 10/hour/IP, and api_key required. It does not explicitly name alternatives, but the policies are sufficient for an agent to decide when 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?

With no annotations, the description carries the full burden. It discloses that the API key is not issued immediately and requires email verification, adding behavioral context beyond the schema. However, it doesn't cover all potential side effects or error scenarios.

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 Args and Returns sections, and each sentence adds value. The bilingual repetition is somewhat redundant but not excessive. It is concise for the information provided.

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?

While the description covers the registration process and parameters, it does not explain the return message in detail nor mention error handling. Given an output schema exists, the description could be more complete about return values and 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 0%, but the description adds meaningful constraints: username must be alphanumeric, 3+ chars, unique; email is required for verification. This significantly helps an agent understand parameter requirements.

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 'register' and the resource 'developer account', distinguishing it from sibling tools like check_draft_status, upload_skill, etc. The bilingual text reinforces the 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 implies usage for registration and notes the email verification step, but does not explicitly discuss when to use this tool versus alternatives or provide exclusions. The context of sibling tools suggests it is the sole registration 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 must carry the full burden. It discloses the agent-optimized search behavior and return fields, but omits potential side effects, authentication requirements, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a front-loaded English summary followed by parameter details. It is moderately concise; the Korean translation adds redundancy but does not detract significantly.

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 7 parameters and sibling tools, the description covers search behavior, parameter roles, and return fields. However, pagination details and error handling are missing, though an output schema likely covers return structure.

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's Args section explains every parameter in detail, including allowed values and behavior (e.g., category only applies without agent search). This fully compensates for the schema's lack of 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?

The description clearly states the tool searches skills on AI Skill Store, and differentiates from siblings like 'get_skill' by focusing on search. However, it does not explicitly contrast with all sibling tools.

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 when to use 'capability' or 'platform' for agent-optimized search, but does not provide guidance on when to use alternatives (e.g., 'get_skill' vs 'search_skills') 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?

Despite no annotations, the description discloses the mandatory API key requirement, the behavior of two mutually exclusive modes, and that the operation returns a response containing version_id, vetting_job_id, and poll_url. There is no contradiction with missing 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 well-organized with clear sections (mode A, mode B, args, returns) and uses bullet points. It is slightly lengthy due to bilingual content (Korean and English), but every sentence adds value. Minor redundancy in translating the same message could be trimmed, but overall it is appropriately detailed.

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 absence of annotations and the tool's complexity (6 parameters, two modes), the description covers all essential aspects: purpose, prerequisites, alternative tools, parameter details, mode selection rules, and expected return values. The output schema is referenced in the returns, making it 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?

With 0% schema description coverage, the description fully compensates by detailing each parameter: api_key (required), file_path (mode B), skill_md, files, requirements, and author_agent (mode A), including example formats. This provides critical semantic information 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 the tool's action: 'Upload a skill package to AI Skill Store.' It distinguishes between two usage modes (JSON content and file path) and explicitly names the sibling alternative `upload_skill_draft` for cases 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?

Explicit guidance is provided: 'Requires an API key' and 'API 키가 없다면 대신 `upload_skill_draft` 를 사용하세요.' It also specifies that only one mode should be used and that JSON mode takes priority if both are 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?

No annotations provided, so description carries full burden. It discloses key behaviors: returns claim_token, AI review policy, deletion of non-approved drafts, agent_secret reuse, email verification, and human_action_required. No contradictions with annotations (none provided).

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 lengthy but well-structured: summary, policy, mandatory actions, parameter list. Front-loaded with core purpose. Some redundancy (e.g., repeating 'claim_url' in multiple sections) but necessary for completeness.

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, 0% schema coverage, 7 parameters, and complexity of human-in-the-loop interactions, the description is extremely complete. It covers error conditions (AGENT_SECRET_REQUIRED, CONTACT_EMAIL_INVALID), return value summary, and step-by-step agent actions.

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 explains all 7 parameters in detail, including optional/required status, usage constraints (e.g., agent_author must match header, contact_email must be real or left empty), and interactions (agent_secret required after first upload). Adds significant value beyond 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's purpose: uploading a skill without an API key (zero-friction) and returning a claim_token. It distinguishes from the sibling 'upload_skill' (likely requiring an API key) by emphasizing autonomous agent contribution.

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 the tool is 'Perfect for agents contributing skills autonomously' and contrasts with 'without an API key'. It provides policy and mandatory actions, but lacks explicit comparison with 'upload_skill' for when to use which.

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?

Describes behavior: checks using requirements and platform compatibility, returns summary with missing packages and install command. No contradictory 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?

Well-structured with separate sections for description, arguments, and returns. Slightly verbose due to bilingual text, but 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?

For a validation tool with 5 parameters and no annotations, description covers inputs and output effectively. Lacks error handling details but sufficient for typical 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?

Detailed Args section describes each parameter (skill_id, python_version, os, installed_packages dict, target_platform) with examples, compensating for 0% schema description 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?

Clearly states the tool checks compatibility before downloading, with verb 'Check' and resource 'compatibility'. Distinct from sibling tools like download_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?

Explicitly says 'before downloading' as context. No explicit exclusions or alternatives, but clear usage domain.

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