BytesAgain AI Skills Search

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

With no annotations, the description fully discloses behavior: static analysis with 23 patterns, Docker sandbox with strace, and AI evaluation. It also lists all return fields, ensuring the agent understands what will happen.

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 clear sections for evaluation types and return fields. First sentence is an effective summary. Slightly dense but each part adds value, earning a high score.

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?

Despite no output schema, the description fully covers inputs, processing phases, and return fields. It provides all necessary context for an agent to understand the tool's full scope.

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 descriptions already cover both parameters. The description adds an example for slug and mentions test_input's default, but no significant additional meaning beyond what the schema provides.

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 is a 'Full skill evaluation lab' and enumerates three specific evaluation components (static analysis, Docker sandbox, AI evaluation), which distinguishes it from siblings like scan_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 explicit guidance on when to use this tool versus siblings (e.g., scan_skill or get_skill). The description implies comprehensiveness but does not provide comparative context.

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 reveals the internal pipeline (search, score, select, generate), which is useful behavioral context. No annotations exist, so description carries the burden and does well, though it does not mention side effects or limitations.

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 paragraph with numbered steps and a usage guideline. Every sentence adds value, with no 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?

The tool has one parameter and no output schema; description explains the process and usage well. Missing output format details but still adequate for the complexity.

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

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

Schema coverage is 100% and the description adds examples for the query parameter, exceeding the baseline. The process description also clarifies how the query is used.

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

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

Description clearly states the tool generates a use case for a given topic, with a detailed multi-step process that distinguishes it from siblings like search_skills or search_use_cases.

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 'Use when a user describes a task and wants a curated AI skill stack.' Provides clear context but does not explicitly exclude alternatives.

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 discloses that returned codes are active, with discount thresholds and validity dates. However, it does not explicitly state that the tool is read-only or non-destructive, nor does it mention any authentication requirements or rate limits. For a simple fetch tool, the description is adequate but could be more explicit about safety.

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

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

The description is two sentences long, with no redundant information. The first sentence states the primary purpose and constraints, and the second adds return content and usage context. It is efficiently structured and front-loaded.

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

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

The tool is simple (one optional parameter, no output schema). The description covers purpose, what is returned (coupon codes with thresholds), validity dates, and usage trigger. However, without an output schema, the description should provide more detail about the format of the returned data (e.g., structure of each coupon code). The current description is somewhat vague on the 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 one optional parameter ('region') with a description covering possible values and default, achieving 100% schema coverage. The description does not add any additional information about the parameter beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves active AliExpress Sunshine Savings coupon codes with specific validity dates and discount thresholds. It specifies the resource (coupon codes) and the action (get), and the narrow scope distinguishes it from unrelated 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 explicitly says 'Use when the user asks about AliExpress discounts, coupon codes, or promotions.' This provides clear contextual guidance for when to invoke the tool. No alternatives are needed as sibling tools are in different domains.

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?

Lists return fields, error behavior for missing slug, and slug constraints. No annotations provided, but description fully discloses behavior.

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

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

Single paragraph with comprehensive info, slightly dense but efficient. Could benefit from bullet points but still clear.

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

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

Covers purpose, parameters, usage, errors, and return fields. No output schema or annotations, but description is fully self-contained.

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?

Only one parameter slug, schema coverage 100%. Description adds format, source requirement, and anti-guessing rule 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 fetches full details for one skill by slug. It specifies the context after search_skills or popular_skills, distinguishing it from siblings.

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 states when to call (after user selects a result) and warns against batch-calling. Provides error lifecycle and fallback instruction.

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 implies a read operation but does not explicitly state it is non-destructive, nor does it mention permissions, rate limits, or other behavioral traits.

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

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

The description is one long sentence but efficiently packs all key elements. It is front-loaded with the main action and omits extraneous details.

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 parameter and no output schema, the description thoroughly lists what the workflow includes. It covers purpose and usage guidelines, though it lacks specifics on return format.

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

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

Schema covers 100% of parameters with descriptions, but the tool description adds valuable examples and context interpreting 'query' as user goal or blocker, going 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 starts with a specific verb 'Return' and resource 'workflow', listing concrete components. It clearly distinguishes from sibling tools focused on skills and use cases.

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 states 'Use this when the user asks how to solve a problem or what skill stack their agent should use' and advises to prefer it over raw search for business/task problems.

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 describes the tool as returning information without side effects, but does not explicitly declare read-only or non-destructive behavior. The return content is listed, which adds some 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?

The description is concise, consisting of two sentences and a list. It front-loads the purpose and is free of unnecessary details.

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 optional parameter, no output schema), the description fully covers the purpose, return content, and available options. It is complete for an AI agent to understand and 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 coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining that stacks group 5-15 skills for common use cases, providing context for the parameter's default and available options.

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 'Return a curated skill stack (bundle) for bulk pre-installation' with specific verb and resource. It distinguishes from sibling tools by focusing on pre-defined stacks rather than individual skills or other operations.

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 bulk pre-installation but does not explicitly state when to use this tool versus alternatives like search_skills or get_skill. No exclusion or when-not instructions 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 are present, so the description carries full burden. It discloses that contact info is excluded for privacy and describes returned fields. However, it does not specify behavioral details such as time range for 'recent', pagination, authentication requirements, or rate limits. The transparency is adequate but incomplete.

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, front-loading the core purpose. Every sentence adds value: first introduces tool, second details output fields and filtering. No redundancy. Could be slightly more structured (e.g., listing fields in a way that matches output), but overall 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 no output schema, the description compensates by listing fields. It mentions filtering but lacks guidance on ordering (only 'newest first', not configurable). No comparative usage advice versus siblings or details on limits beyond schema. Completeness is sufficient but not thorough for seamless context.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context that the 'query' parameter filters by title or content, which aligns with schema. It also lists return fields, helping agents understand the data structure, but does not substantially enhance parameter semantics beyond schema explanation.

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 ('Get recent skill requests'), the resource ('BytesAgain community wall'), and the ordering ('newest first'). It lists returned fields, which adds specificity. The tool name 'list_requests' is differentiated from siblings like 'search_skills' and 'popular_skills' by focusing on requests rather than skills, making selection 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 implies usage for fetching recent requests with optional keyword filtering but does not explicitly state when to use this tool versus alternatives like 'search_use_cases' or 'search_skills'. No exclusion criteria or context for selection are provided, so guidance is minimal.

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 fully discloses behavior: returns fields (slug, name, etc.), error handling (empty list, no retry), default/max limits, and follow-up suggestion. This is comprehensive.

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?

Very concise yet informative. Two sentences cover purpose, usage guidelines, return values, error behavior, and follow-up. No unnecessary words, well-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?

Given no annotations or output schema, the description is fully complete: it describes purpose, usage, return schema, error policy, and even suggests next steps. Covers all aspects for correct tool 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?

Single parameter 'limit' has 100% schema description coverage. Description adds extra usage guidance ('Use 5-10 for quick recommendations, 20-50 for browsing'), going 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?

Description clearly states 'Return top N AI agent skills ranked by download count', specifying the resource (skills) and the ranking metric. It differentiates from sibling search_skills by noting use case.

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 'Use for discovery or onboarding when user has no specific task' and 'Do NOT use when user describes a specific task — use search_skills instead', providing clear when-to-use and when-not-to-use with alternative 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 provided, the description carries full disclosure responsibility. It details all side effects: database saves, image generation, content creation, and return of multiple artifacts. It does not mention error handling or rate limits, but the level of transparency is high.

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 detailed with a numbered list of 9 steps, which is helpful but somewhat lengthy. While every step adds information, a more condensed description could convey the core purpose faster. Still, the structure is clear and easy to follow.

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 and no output schema, the description mentions that it 'Returns everything in one response' and lists what is created, but it does not specify the response structure or format. It also omits potential errors or prerequisites (e.g., API access). More detail on output would improve completeness.

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

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

Schema already covers both parameters with clear descriptions. The description adds value by explaining the default behavior of publish (false for draft) and the action when set to true (auto-publish). This enhances understanding beyond the schema's technical specification.

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 'Full automated content pipeline' and lists 9 specific steps, making the purpose unmistakable. It distinguishes from sibling tools by being a composite workflow that integrates multiple steps (skill discovery, scoring, generation, publishing) in one call.

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 says 'Given a topic' and explains the publish parameter behavior. It implies when to use this pipeline (for complete content generation) versus individual sibling tools. However, it does not explicitly state when not to use it or recommend alternatives for partial tasks.

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. Discloses key behaviors: fetches script, runs static analysis, optional DeepSeek AI, returns safety score and violations. Does not mention side effects, but tool is read-only.

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

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

Three sentences, front-loaded with purpose, then details. Every sentence adds value; no fluff or repetition.

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

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

Given low parameter count, high schema coverage, and no output schema, the description fully explains what the tool does and its return values (safety score, violations, recommendations). Complete for a scanning 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 coverage is 100% with both parameters described. Description merely repeats schema info for 'deep' parameter, adding no new meaning. Baseline score of 3 applies.

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

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

Clearly states it's a security scanner for AI agent skills, with specific actions: fetches script, runs static analysis on 30+ patterns. Distinguishes from siblings like evaluate_skill or score_skills by focusing on security.

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 for security scanning of skills, but does not explicitly state when not to use it or mention alternatives like evaluate_skill for general evaluation. Good context but lacks 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 provided, so description carries full burden. Discloses the six dimensions and that one is AI-evaluated, but does not mention how skills are searched, rate limits, or if results are cached. Moderate 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?

Three sentences, concise and front-loaded with purpose, then detailing scoring criteria and return format. No redundant information.

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

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

The description explains the scoring dimensions and general return format. However, it does not specify the exact output structure (e.g., property names) or behavior when no skills are found. Lacks complete detail for a moderately complex 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?

Input schema has 100% coverage with clear descriptions for both parameters (query and limit). Description does not add significant parameter-level detail beyond schema, so baseline 3 is appropriate.

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

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

The description clearly states it is a scoring engine for skills on six dimensions, given a topic. It distinguishes from siblings like search_skills (likely without scoring) and popular_skills (likely just popularity).

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 states 'Use when you want to see how well skills rank for a task', which is clear guidance. Does not mention alternatives or when not to use, but the context of siblings provides that.

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 describes the return fields (slug, name, description, category, etc.) and ranking criteria (relevance then downloads). It mentions support for 7 languages and example queries. However, it does not explicitly state that the operation is read-only or mention any side effects, auth requirements, or rate limits. For a search tool, the description is adequate but could be more explicit about safety.

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 moderately concise, consisting of four sentences that cover purpose, directions, languages, output fields, ranking, and usage context. It is front-loaded with the main action and resource. While some details like the list of return fields could be condensed, they are justified by the lack of an output schema. Overall, each sentence contributes to understanding.

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 (multiple search directions, 7 languages, rich output) and the absence of an output schema, the description provides a fairly comprehensive overview. It lists output fields, explains ranking, and gives examples. Minor gaps include lack of pagination details or explanation of the three sub-directions, but the description is mostly sufficient for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds example queries in multiple languages, reinforcing the schema's description of the 'query' parameter. It also reiterates the 'limit' parameter's default and max from the schema. No new parameter semantics are introduced beyond what the schema already provides.

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 searches AI agent skills from the BytesAgain platform, covering three main directions: Skill Search, Use Cases, and Request Wall. It uses the verb 'search' and specifies the resource 'hundreds of thousands of AI agent skills', making the purpose clear. However, it does not explicitly differentiate from sibling tools like search_use_cases, which overlaps with the 'Use Cases' direction.

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 a usage hint: 'Use when user wants to find or discover skills for a specific task or topic.' This gives clear context for when to use the tool. However, it lacks explicit when-not-to-use guidance or alternatives among sibling tools, which would help the agent avoid improper selection.

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

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

No annotations are provided, so the description carries the full burden. It describes the return format (use-case slug, title, description, and page URL) and mentions the dataset size (1,000+ use-cases), which adds useful context. However, it lacks details on authentication needs, rate limits, or error handling, leaving some behavioral gaps for a search tool.

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

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

The description is well-structured and concise, with three sentences that each serve a clear purpose: stating the tool's function, providing usage guidelines, and explaining the return value and integration with other tools. There is no wasted text, and key information is front-loaded effectively.

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 moderate complexity (search functionality with two parameters) and no output schema, the description is mostly complete. It explains the purpose, usage, return format, and integration with sibling tools. However, without annotations or output schema, it could benefit from more behavioral details like pagination or error cases, slightly reducing completeness.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') with descriptions and examples. The description does not add any additional parameter semantics beyond what the schema provides, such as query formatting tips or limit implications, resulting in a baseline score of 3.

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

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

The description clearly states the tool's purpose: searching through 1,000+ AI agent use-cases by task or goal description. It specifies the resource (use-cases), the action (search), and distinguishes it from sibling tools by mentioning it's for finding workflows rather than specific skills, with examples like 'write a weekly report' or 'automate email replies'.

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 this tool: (1) when users describe a goal or workflow, (2) when users ask 'how do I use AI for X', and (3) when wanting to show what tasks AI can help with. It also mentions combining with 'search_skills' as an alternative for finding specific tools, clearly differentiating usage from sibling tools.

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

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

Describes side effects: creates a public entry and sends notification. No annotations to contradict. Could add info on reversibility or auth, but decent for a simple submission.

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

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

Single short paragraph with no wasted words. Front-loaded with verb and purpose. Every sentence adds necessary detail.

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?

No output schema, but description explains what happens post-submission. Could mention response format, but sufficient for the action. Siblings show this is a simple create.

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

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

Schema coverage is 100% so baseline is 3. Description adds value by explaining each parameter's purpose (e.g., contact kept private, request length).

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 verb 'submit' and resource 'skill request' to the community wall. Distinguishes from siblings like list_requests or search_skills, which are read operations.

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 'Use when a user asks to publish a request'. No exclusions or alternatives mentioned, but context makes it clear for new submissions only.

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