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

No annotations are provided, so the description carries full burden. It describes the return types (active, deceased, acquired) and associated data (date, cause, alternatives), but lacks details on authentication, rate limits, or precise response format. Acceptable for a simple status check.

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: first explains what it does and returns, second provides usage guidance. No redundancy, front-loaded with essential information.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers key aspects: purpose, return values, and usage context. Minor details like error handling or exact date format are omitted but not critical.

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 a single parameter. Description provides an example ('Jasper' or 'jasper-ai') adding mild extra value, but mostly restates the schema description. 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?

Description clearly states the tool checks whether an AI tool is 'still alive' and returns statuses (active, deceased, acquired) with details. It distinguishes from sibling tools like 'list_tools' or 'search_tools' by focusing on a single tool's viability.

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

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

Explicitly advises using this tool before recommending a tool to avoid suggesting defunct ones. Though it doesn't explicitly mention when not to use, the context is clear enough for an agent.

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 return items (profiles, verdict) and the conditional existence of a head-to-head verdict. However, it does not mention potential side effects, authentication needs, or rate limits, leaving behavioral traits partially uncovered.

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, front-loaded with the main action and followed by output details. Every word is relevant and no superfluous information, achieving high conciseness.

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 adequately covers the tool's output for a simple comparison tool with two required parameters and no nested objects. It mentions the return fields (pricing, rating, etc.) but does not cover error conditions or slug existence handling. Given no output schema, the description provides sufficient completeness 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?

Schema description coverage is 100% with both parameters already described. The description adds that slugs are 'directory slugs,' which clarifies the parameter values but does not provide new semantic meaning beyond what the schema already conveys.

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: compare two AI tools by directory slugs. It specifies the verb 'compare' and the resource 'AI tools,' and it distinguishes from siblings like 'get_tool' (single tool) and 'find_alternatives' (alternative 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 implies when to use the tool (for side-by-side comparison) but does not explicitly state when not to use it or mention alternatives like 'find_alternatives' or 'search_tools.' It lacks explicit guidance on selection criteria.

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

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

No annotations provided, so description carries burden. Mentions shutdown-specific behavior ('returns live replacements') but omits details like result format, error handling, or rate limits. Acceptable 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?

Two efficient sentences: first states purpose and method, second adds key conditional. No wasted words, front-loaded with essential info.

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 complexity (1 param, no output schema), description covers core purpose and a behavioral nuance (shutdown). Lacks return format details but sufficient for a lookup tool.

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

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

Schema description coverage is 100% for the single 'slug' parameter. Description adds context ('directory slug', AI tool scope, conditional behavior) beyond the schema, enhancing understanding.

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 'find' and resource 'alternatives to a given AI tool', with specific method (by directory slug) and conditional behavior (shutdown replacement). Distinguishes from siblings like 'check_tool_status' and 'compare_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?

Explicitly says 'Good for "what should I use instead of X" questions', giving clear use case. Does not mention when not to use or alternatives, but context is sufficient.

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 lists the return fields comprehensively, implying a read-only operation. No contradictions or hidden behaviors are omitted.

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 sentence that efficiently conveys purpose and return fields. It could be slightly more structured by separating input and output, but it remains concise 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 description adequately explains both input (slug from search_tools) and output (list of fields) for a single-tool retrieval tool. No output schema exists, so the description fills that 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?

The schema already describes the slug parameter with 100% coverage, but the tool description adds context: it provides an example slug and explains the slug originates from search_tools. This adds 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 the tool retrieves the full profile of one AI tool by its directory slug, listing specific fields (description, pricing, key features, etc.). This distinguishes it from siblings like list_tools or search_tools which are for multiple 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 mentions using the slug from search_tools, indicating a clear workflow. It does not provide explicit when-not-to-use instructions, but the context is 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?

No annotations are provided, so the description carries full burden. It discloses that tools are 'top-rated' and 'active', which adds behavioral context, but does not explain what 'top-rated' means, whether results are sorted, or if any rate limits or authentication are 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?

Two sentences, no fluff. The first sentence states the core function and filters; the second gives concrete examples. Front-loaded and 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?

The tool has 4 parameters with good schema coverage but no output schema. The description does not describe the return format, pagination behavior, or what happens when no results are found. Given the complexity, more detail is needed for 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 each parameter is already documented in the schema. The description adds minimal extra meaning beyond the schema, such as the example phrasing, 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 uses a specific verb ('List') and resource ('top-rated active AI tools'), and gives example queries ('best AI tools for sales', 'free tools in <category>') that clearly differentiate it from siblings like search_tools and get_tool.

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 ('good for...') but does not explicitly state when not to use or contrast with alternatives like search_tools or find_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, the description adds context about hybrid semantic search and return fields (slug, description, pricing, rating). It does not disclose potential side effects (none expected), rate limits, or authentication needs. Adequate but not thorough.

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

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

Two sentences, front-loaded with the verb and resource, no unnecessary words. 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?

Given no annotations or output schema, the description covers the essential purpose, return values, and usage flow. It could mention that it's read-only or clarify ranking, but overall it is sufficiently 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 the schema already describes both parameters. The description adds that it uses hybrid semantic search and returns ranked tools, which provides minor additional context. 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 searches the AI Tool Directory catalog by keyword, use case, or category, using hybrid semantic search. It distinguishes from sibling tools by positioning itself as the discovery tool, with 'then get_tool for full detail' indicating a workflow.

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 this to discover tools, then get_tool for full detail,' which provides clear guidance on when to use this tool vs. get_tool. However, it does not mention other siblings like list_tools or find_alternatives, leaving some ambiguity.

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