BytesAgain AI Skills Search

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

With no annotations, the description carries the full transparency burden. It discloses the evaluation pipeline (static analysis patterns, Docker sandbox with strace and syscall monitoring, AI evaluation) and the return structure, giving a solid sense of what happens. It does not mention potential side effects or prerequisites (e.g., Docker availability), but covers the main behaviors well.

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 dense and information-rich, front-loaded with the main purpose and then structured around the three evaluation phases. Every sentence contributes value, though the long return-fields list makes it slightly heavy. It is concise given the tool's complexity.

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 having no output schema, the description fully spells out the return fields (safety_score, risk_level, execution_report, evaluation with all subfields). It also explains the three evaluation stages, making the tool's behavior and outputs clear. This is robust for a multi-phase evaluation 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 ('slug' and 'test_input') having descriptive comments. The description adds no extra parameter semantics, but since the schema is complete, the baseline of 3 is appropriate.

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

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

The description clearly states what the tool does: it is a 'Full skill evaluation lab' with three concrete phases (static analysis, Docker sandbox execution, AI evaluation). It distinguishes itself from siblings like 'scan_skill' by emphasizing comprehensiveness (static analysis patterns, sandbox execution, AI evaluation).

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 says this is a 'Full skill evaluation lab', which clearly signals when to use it over simpler tools like 'scan_skill' or 'score_skills'. It does not explicitly name alternatives or exclusions, but the context is strong enough to infer the intended use case.

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 transparency burden. It discloses the internal process (search 60,000+ skills, AI-score, select top 5, generate use case), which gives the agent a strong expectation of how the tool behaves. It could be improved by mentioning whether it performs any side effects or how long it takes, but the process disclosure is substantial.

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: a one-sentence summary followed by a numbered process list and a usage note. Every sentence adds value, and the front-loaded verb 'generate' immediately clarifies purpose. 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 the tool's simplicity (1 required parameter, no output schema, no annotations), the description is sufficiently complete. It explains the process, usage context, and hints at the output ('structured use case with skill recommendations'). A minor gap is the lack of detail on the output structure, but this is not critical for 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 coverage is 100% with the 'query' parameter fully described. The description does not add significant parameter semantics beyond the schema, only reinforcing 'given topic or goal' and providing examples in the schema. As per the baseline for high coverage, score 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's function: 'Generate a use case for a given topic or goal.' It also outlines a concrete multi-step process (search, score, select, generate), distinguishing it from sibling tools like search_skills or score_skills. The verb 'generate' and resource 'use case' 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 provides explicit guidance: 'Use when a user describes a task and wants a curated AI skill stack.' This clearly indicates the appropriate context. However, it does not explicitly mention alternatives or when not to use this tool, though siblings like search_skills are implied as alternatives for narrower 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?

With no annotations, the description carries the burden. It clearly indicates a read-only operation via 'Get' and 'Returns', and adds context about the nature of the data (active, valid dates, discount thresholds). While it doesn't discuss side effects, pagination, or error handling, these are less critical for a simple coupon lookup. The description provides clear behavioral context beyond a basic read/write label.

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, each earning its place: first states the primary function, second clarifies the return value, third gives usage guidance. No fluff, front-loaded with the most critical information.

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

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

For a simple tool with one optional parameter and no output schema, the description provides sufficient context: what to expect (coupon codes with thresholds), when to use it, and validity period. It does not mention the region parameter, but the schema fully covers that. The description is complete enough for an agent to select and invoke 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 100% for the single optional 'region' parameter. The description does not mention the parameter or add any meaning beyond the schema, so the baseline of 3 is appropriate.

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

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

The description clearly states the specific action ('Get active AliExpress Sunshine Savings coupon codes') with precise resource identification, including discount range and validity dates. It is easily distinguished from all sibling tools, which focus on skills, workflows, and pipelines rather than deals.

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 an explicit usage trigger: 'Use when the user asks about AliExpress discounts, coupon codes, or promotions.' However, it does not mention when not to use it or name any alternative tools, so it lacks the 'when-not/alternatives' component for a 5.

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

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

With no annotations, the description carries full behavioral burden and delivers: it lists the return fields, defines the error lifecycle for a missing slug, and warns against guessing slugs. This is strong behavioral disclosure for a simple read 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 compact and front-loaded with the core purpose, followed by terse guidance and error behavior. Every sentence carries meaningful information, and the structure flows logically from purpose to usage to fallback.

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 read tool with no output schema, the description is complete: it enumerates return fields, explains the error response, and positions itself within the tool family. No critical behavioral or usage detail is missing.

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 already documents the slug format and requiredness at 100% coverage. The description adds useful integration context ('only use slugs from prior tool results') and reinforces the 'never guess' rule, adding modest semantic 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 starts with a specific verb+resource: 'Fetch full details for one skill by slug.' It clearly distinguishes from sibling tools like search_skills and popular_skills by emphasizing single-skill detailed retrieval rather than list or search behavior.

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 when to use this tool: 'Call AFTER search_skills or popular_skills when a user selects a specific result.' It also provides a negative directive ('do NOT batch-call for every item') and a concrete fallback path to search_skills on error, making usage guidance exceptionally clear.

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

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

With no annotations, the description carries the disclosure burden. It explains the tool returns a structured workflow and lists its components, which is meaningful behavioral context. However, it does not explicitly state whether the operation is read-only, if any side effects occur, or how the workflow is generated, though 'Return' implies retrieval. Slightly falls short of full transparency for a no-annotation 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 two sentences with no filler. The first sentence efficiently lists the workflow components, and the second provides usage guidance. Every phrase contributes to understanding, making it appropriately sized and 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?

For a single-parameter tool with no output schema, the description is sufficiently complete. It explains what the tool does, when to use it, and what the returned workflow includes. It also distinguishes from alternatives, covering all necessary context for an agent to invoke it 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 schema fully describes the single 'query' parameter with examples, so the baseline is 3. The description does not add additional parameter semantics beyond saying the workflow is for a 'user goal,' which is already covered by the schema. No value added 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 tool returns a complete agent-ready workflow for a user goal, and enumerates what the workflow includes (who it's for, blockers, skill selection standards, steps, candidate stacks, prompt, upgrade path). This specific verb-resource combination distinguishes it from siblings like get_skill or search_skills, and explicitly contrasts with raw search.

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 when-to-use guidance: 'Use this when the user asks how to solve a problem or what skill stack their agent should use' and directs preference over raw search for business/task problems. This clearly frames usage context and 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?

With no annotations provided, the description carries the full burden. It transparently states that the tool returns a stack and describes the return fields ('stack name, description, skills with slugs, install commands'). It stops short of explicitly stating that no installation occurs, but the verb 'Return' makes this apparent. The description provides useful behavioral context beyond the schema.

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

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

The description is front-loaded with the primary purpose, then provides brief elaboration and the list of available stacks. Every sentence earns its place, and there is no fluff or redundancy. The structure is exceptionally clear and easy to parse.

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

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

For a tool with one optional parameter, no output schema, and no annotations, the description is highly complete. It explains what the tool does, describes the return shape, lists all available stacks, and covers the key use case. The absence of output schema is mitigated by the explicit return fields. This is complete enough for an agent to invoke 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% for the single 'name' parameter, including the list of available stacks and the default value. The description redundantly lists the stacks and adds context that each stack groups 5-15 skills, but it does not add new parameter-level meaning beyond what the schema already provides. Baseline 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?

The description clearly states the tool's function: 'Return a curated skill stack (bundle) for bulk pre-installation.' The verb 'Return' and resource 'skill stack' are specific, and listing available stacks adds scope. This distinguishes it from sibling tools like get_skill or search_skills by focusing on multi-skill bundles.

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 conveys the use case: 'for bulk pre-installation' with each stack grouping 5-15 skills for a common use case. It does not explicitly mention when not to use it or name alternative tools, but the context is clear enough for an agent to select this when a predefined bundle is 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 carries the full transparency burden. It explicitly states ordering ('newest first'), return fields, privacy handling ('Contact info is excluded for privacy'), and optional filtering. It does not mention authentication or side effects, but the read-only nature is clear and no destructive behavior is implied.

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, and every sentence provides value: what it does, what it returns, and how to filter. 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?

No output schema, so the description compensates by listing return fields explicitly. It lacks details on pagination or how to get older requests, and the response shape (array vs object) isn't stated. For a straightforward list tool, this is largely sufficient but not fully 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 coverage is 100%, so the schema already documents both parameters. The description adds that query filters by 'title or request text', which is present in the schema description too. No substantial new meaning is added, 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 starts with a specific verb and resource: 'Get recent skill requests from the BytesAgain community wall, newest first.' This clearly differentiates from sibling tools like search_skills (skills) and submit_request (creating requests). The return fields and filter option further clarify scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: to retrieve recent skill requests, with optional keyword filtering. It doesn't explicitly exclude alternatives or name sibling tools, but the 'community wall' and 'requests' framing make it distinct from skill-focused 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?

With no annotations, the description carries full disclosure burden. It reveals that database errors return an empty list and instructs not to retry, documents the exact return fields (slug, name, description, category, downloads, stars), and specifies default and max limits. This is non-obvious, valuable behavioral context that goes beyond the schema.

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

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

Description is front-loaded with purpose, followed by essential guidance on when to use, what it returns, error handling, and follow-up actions. Every sentence contributes actionable information without waste, maintaining a clear structure.

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 simple tool with one optional parameter and no output schema, the description covers return values, default/max, error behavior, and use cases, making it fully self-contained. It even specifies when to escalate to other tools, ensuring the agent has all context needed for correct invocation and interpretation.

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 already fully documents the single optional parameter (limit) including default, max, and usage guidance (5-10 for quick recommendations, 20-50 for browsing). The description merely restates the default/max, adding no additional semantics, so a 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 returns top N AI agent skills ranked by download count, using a specific verb and resource. It explicitly distinguishes itself from search_skills and get_skill, eliminating ambiguity among 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?

Provides explicit use cases ('discovery or onboarding when user has no specific task in mind') with example queries, and explicitly tells when NOT to use it (specific task -> search_skills). Also advises following up with get_skill only when details are requested, offering clear decision boundaries.

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 side effects: saving to Supabase posts and use_cases tables, uploading cover image to storage, and auto-publishing behavior controlled by the publish parameter. This transparency about mutations (writes, uploads) and default draft-only mode exceeds typical descriptions.

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 longer than average, but the numbered steps make it scannable and information-dense. Every sentence contributes: the opening defines scope, steps detail the process, and the final sentence clarifies the publish flag. Acceptable for a complex pipeline tool.

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 2-parameter tool with no output schema, the description covers inputs, the full sequence of operations, data persistence effects, and publishing behavior. It does not describe the response format in detail, but 'Returns everything in one response' gives adequate closure. The absence of error handling discussion is a minor gap.

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% for both parameters, but the description enriches semantics by stating 'Given a topic' and providing an example for query, and explaining the publish parameter's effect ('Set publish=true to auto-publish'). This adds practical meaning beyond the schema's simple type 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 opens with 'Full automated content pipeline' and enumerates a 9-step process, clearly specifying the tool's comprehensive behavior. It distinguishes itself from sibling tools like generate_usecase or score_skills by covering the entire workflow from skill discovery to publishing, making its purpose 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 states the tool takes a topic and produces a complete article with use case and tweets, implying use when a full content pipeline is desired. It does not explicitly mention alternatives or when NOT to use it, but the 'Full automated' framing sets clear context against more granular 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?

With no annotations, the description carries full responsibility for disclosing behavior. It does so effectively by explaining the workflow: fetches the script, runs static analysis on 30+ patterns, uses pattern matching with optional DeepSeek AI, and returns a score, violations, and recommendations. It does not mention potential failures or limitations, but the core behavior is well covered.

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

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

The description is a single paragraph that is information-dense yet concise. It front-loads the core purpose and provides concrete examples of threat patterns. While it is slightly long, every sentence adds value, making it well-structured for an agent to parse key details quickly.

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

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

Given the tool's moderate complexity and lack of an output schema, the description adequately covers inputs, process, and outputs (safety score, violations, recommendations). It does not address edge cases like nonexistent skills or error handling, but the essential information for an agent to invoke the tool correctly is present.

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 already provides thorough descriptions for both parameters (slug and deep), achieving 100% schema description coverage. The description reinforces the overall purpose but does not add additional parameter-level semantics beyond what the schema offers, 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 is a 'Security scanner for AI agent skills' and details its specific actions: fetching the skill's script and running static analysis for dangerous patterns. It distinguishes itself from sibling tools like evaluate_skill or score_skills by focusing on security vulnerabilities and providing a safety score, making its purpose 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 establishes clear context for when to use the tool: whenever a security assessment of an AI agent skill is needed. It does not explicitly mention exclusions or alternatives, but the detailed focus on threat patterns and the output format imply the intended use case strongly enough that an agent can select it appropriately.

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 available, the description carries the full burden. It discloses the algorithm, including weighted scoring and AI-evaluated relevance, and states the output format ('Returns scored list sorted by total'). It does not explicitly mention being read-only, but the search/score nature implies no side effects.

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, front-loaded with the core purpose, and efficiently packs scoring dimensions, weights, usage, and output into minimal text. Every sentence provides actionable information without 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?

The description covers purpose, usage, scoring logic, and output type concisely. Since there is no output schema, it explains the return as a 'scored list sorted by total' but could be more explicit about the structure of each scored item; still, it is adequate for the agent's selection and 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 coverage is 100% and both 'query' and 'limit' already have clear descriptions. The description adds little beyond aligning 'Given a topic' with the query parameter; it does not enrich parameter meaning further.

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

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

The description states a specific verb ('searches skills and scores each') on a specific resource (skills) and details six scoring dimensions with weights. It clearly distinguishes itself from siblings like search_skills and evaluate_skill by emphasizing ranking behavior and the sorted output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'Use when you want to see how well skills rank for a task,' providing clear context. However, it does not mention alternatives or exclusions, so it stops short of full when-not 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 full burden. It discloses ranking behavior (relevance then download count), supported languages, and the platform source. It does not explicitly state it's read-only, but 'Search' implies this. It could mention authentication or rate limits, but for a search tool the transparency is strong.

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 packed with information but remains focused. It front-loads the main purpose and then provides useful details on directions, languages, return fields, ranking, and usage. Every sentence adds value, though the '3 main directions' sentence could be more structured to avoid ambiguity. It's concise relative to the information presented.

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 lists the return fields, ranking, and usage context, which covers most essentials. The only gap is a slight ambiguity about how the '3 main directions' relate to the tool — whether it searches all three or just skills. Minor omission of rate limits or pagination, but overall complete for a search 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%, so baseline is 3. The description adds value by explaining query supports 7 languages and provides concrete multilingual examples (e.g., 'email automation', '邮件自动化'). It also clarifies ranking behavior, which indirectly helps understand the 'limit' parameter. The schema already documents limit default and max, so this exceeds the baseline.

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 opens with a specific verb and resource: 'Search hundreds of thousands of AI agent skills.' It clearly distinguishes the tool from siblings like search_use_cases by focusing on skills and listing the exact return fields (slug, name, description, etc.). Despite a brief mention of '3 main directions,' the core purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use context: 'Use when user wants to find or discover skills for a specific task or topic.' It gives multilingual example queries, but does not explicitly name alternatives or say when not to use this tool versus search_use_cases or other siblings. The guidance is clear, but lacks explicit 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?

With no annotations provided, the description carries the full burden. It explains the returned fields ('use-case slug, title, description, and page URL') and the relationship to dedicated pages. It does not explicitly state read-only behavior but 'search' implies it; lacks details on pagination or rate limits, which keeps it from a 5.

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, front-loaded with the main purpose, and every sentence adds value. The 'Use this tool when' list is concise and the combined mention of search_skills is relevant. It is appropriately sized for the information needed.

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 (2 params, schema covers them), and the description provides return fields, usage conditions, and sibling relationship. Without an output schema, listing returned fields helps. Minor omissions like pagination are not critical for this search 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?

The schema already covers both parameters with descriptions and examples, providing 100% coverage. The description adds context by framing 'query' as a task/goal description, but no additional parameter semantics 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 identifies the tool's purpose: 'Search 1,000+ AI agent use-cases by task or goal description.' It uses a specific verb ('Search') and resource ('use-cases'), and distinguishes from sibling tools like search_skills by focusing on goals/workflows rather than tool names, mentioning to 'Combine with search_skills.'

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 with 'Use this tool when:' and lists three concrete scenarios (goal/workflow description, asking 'how do I use AI for X', showing what tasks AI can help with). It also directs users to combine with search_skills for specific tools, clearly differentiating use cases.

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 takes on full burden. It discloses important behaviors: creates a public entry, sends notification to site admin, and keeps contact private. This goes beyond basic create semantics and informs the agent of side effects.

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

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

The description is a single paragraph that front-loads purpose and usage, followed by a compact parameter summary. It's slightly long due to enumerating all six params, but each part serves a purpose and no filler exists.

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 6-parameter tool with no output schema and no annotations, the description covers purpose, usage, parameters, and key side effects. It doesn't describe the return value, but for a submission tool this is acceptable without an output schema.

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

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

Schema description coverage is 100%, so baseline is 3. The description restates parameters with brief clarifications (e.g., contact as 'email/TG for follow-up, kept private'), but adds limited new meaning beyond the schema. It's helpful but not significantly additive.

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: submitting a new skill request to the BytesAgain community wall. It uses a specific verb ('submit') and resource ('skill request') and distinguishes from sibling tools like list_requests or search_skills by focusing on creation.

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 use: 'Use when a user asks to publish a request for an AI skill they need.' This provides clear context, though it doesn't mention exclusions or alternatives directly, the intent is unambiguous given the sibling list.

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