Agentic.ai Directory

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

With no annotations provided, the description carries the full burden. It discloses the return format ('structured field matrix and verdict'), implying read-only behavior. However, it does not explicitly state the tool is read-only or mention any side effects, which is a minor gap.

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. It front-loads the core purpose and immediately follows with usage guidance. Every sentence earns its place.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description is complete. It explains the output format and provides usage context. Parameter coverage is full, and behavioral transparency is adequate for a comparison 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 already described clearly in the input schema (string with example slugs). The description does not add extra meaning beyond the schema, 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 compares exactly two AI tools side-by-side, specifying the verb 'compare' and resource 'AI tools'. It distinguishes from sibling tools like search_listings and semantic_search by stating those are for finding tools first.

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 tells when to use this tool ('when a user wants to decide between two specific tools') and when not to ('For finding tools first, use search_listings or semantic_search'). Provides clear 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 must carry full burden. It discloses the output structure (9 dimensions, scores, evidence-based reasoning) and mentions the scoring rubric v3.1. It does not mention side effects or authentication, but as a read-only retrieval, the description is adequate. A slight deduction for not explicitly stating it is non-destructive or 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?

The description is extremely concise at two sentences, front-loaded with the primary purpose, and contains no fluff. 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?

The description covers the output details (9 dimensions, scoring, evidence) despite no output schema. It mentions the rubric version. It does not explain the format of the output (e.g., JSON structure), but it's sufficient for an agent to understand what it will receive. Slight deduction for missing format details.

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 one parameter 'slug' described. The description does not add extra meaning beyond the schema; it merely echoes that the tool is for a single tool. With high schema coverage, baseline is 3, and no additional value is provided.

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) and the resource (full agenticness evaluation breakdown) with specific details: 9 dimensions, scoring 0-4 each, plus evidence-based reasoning. It also explicitly distinguishes this tool from siblings by stating that get_listing provides a quick score and compare_listings is for comparisons.

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 (deep analysis of one tool's AI agent capabilities) and when-not-to-use (for quick score use get_listing, for comparisons use compare_listings) guidance, making it very clear for an agent to decide.

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?

Without annotations, the description carries full burden. It discloses sorting by agenticness score and that only published listings are returned. However, it does not mention pagination, authentication, or rate limits, which would improve transparency for a 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?

Three sentences with no redundant text. The main purpose is front-loaded, followed by usage guidance. 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 the single required parameter, no output schema, and simple task, the description covers browsing a category and sorting. Missing details like pagination or response format, but still largely complete.

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

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

Schema coverage is 100%, so baseline is 3. The description implies the single 'slug' parameter but does not add new semantics beyond the schema's example values and 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 states 'Get all published listings in one specific category, sorted by agenticness score.' It clearly identifies the verb, resource, and scope, and differentiates from siblings like list_categories and search_listings.

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 this to browse a category.' Also provides alternatives: 'To see all categories first, use list_categories. To search across ALL categories, use search_listings or semantic_search.'

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 the description adds crucial behavioral context beyond schema: explains that null on boolean fields means 'unknown' while false means 'confirmed no'. This is valuable for interpreting results. Lacks mention of any side effects, but as a read-only lookup this is acceptable.

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 concise sentences front-loaded with purpose, then usage guidance, then a note on boolean semantics. No fluff.

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 retrieval tool with one required parameter and no output schema, the description sufficiently explains what it returns and provides a key nuance about null/false. Could be slightly more explicit about the output format (single object) but overall adequate.

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 a clear description of the slug parameter. The tool description does not add additional semantic detail beyond what the schema already provides, so 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 tool retrieves full details for one specific AI tool by slug, explicitly mentioning included fields (features, pricing, agenticness scores, structured attributes). It differentiates from siblings like compare_listings and search_listings.

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 (when slug is known), when-not-to (need slug first), and alternatives (search_listings to find slug, compare_listings for comparison). Perfect 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?

No annotations are provided, so the description carries the full burden. It discloses the returned data (descriptions, listing counts) and the category split. However, it does not mention potential pagination, ordering, or any rate limits. For a simple list tool, this is mostly sufficient but lacks a small detail on response structure.

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 three sentences. The main action is front-loaded, and every sentence adds value: first sentence states what it does, second provides usage guidance, third explains additional 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 the tool's simplicity (no parameters, no output schema), the description is complete. It includes enough context for an agent to understand what the tool returns and how it fits with siblings. No additional information is necessary.

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

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

No parameters exist, so per guidelines the baseline is 4. The schema coverage is 100% trivially. The description adds no parameter information, but none is needed since there are no parameters.

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 gets all categories with descriptions and listing counts. It uses the specific verb 'get' and resource 'all categories', making the purpose unambiguous. It also distinguishes from the sibling tool get_category by noting that get_category is for listings within a category.

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 provides when to use this tool ('discover what categories exist before filtering') and when not to ('to get listings IN a category, use get_category'). It also explains the two cohorts (PEOPLE and TEAMS), giving practical context for usage.

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

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

No annotations provided, so description carries full burden. It discloses sorting by creation date and that it shows most recently added, but does not explicitly state it's read-only or whether auth is needed. Adequate but not rich.

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 with no wasted words. First sentence states purpose, second indicates use case, third gives clear alternatives. Information is 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?

For a simple tool with 1 optional parameter and no output schema, the description adequately covers what it returns (listings, sorted by creation date) and the limit parameter. Could mention listing fields but not essential.

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 parameter 'limit' with a clear description. The tool description adds no extra semantic meaning beyond what the schema provides, so 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?

Description clearly states it retrieves recently added AI tool listings sorted by creation date. It also distinguishes from siblings by mentioning search_listings and get_category as alternatives.

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 when to use this tool vs alternatives: for finding specific tools use search_listings, for browsing by category use get_category. Lacks explicit when-not-use but provides clear 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?

No annotations provided, so description carries full burden. It implies a read-only operation but does not explicitly confirm safety, auth requirements, or rate limits. The grouping structure is disclosed but behavioral traits are not fully addressed.

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 with no waste. First sentence defines purpose, second gives usage context, third clarifies limitations. 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?

Even without an output schema, the description sufficiently explains the grouped output and connects to sibling tools. Complete for a simple list tool with no parameters.

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

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

No parameters exist, baseline 4. Description adds value by explaining the output grouping and purpose, which goes beyond the empty schema.

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

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

Description clearly states it retrieves all tags grouped by type, listing specific categories. It distinguishes itself from siblings by explicitly stating it does not return listings, redirecting to search_listings or get_category.

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 to use this to discover available filter values and that tags can be used as filters in search_listings. Also states what it does not do, providing clear 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?

No annotations are provided, so the description carries full burden. It explains the tool returns 'curated, ranked results with explanations' and handles search, filtering, scoring, ranking. No destructive behavior is mentioned; it's presumably read-only. The description could be more specific about output format or rate limits, but overall it's transparent enough.

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

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

The description is concise and front-loaded with purpose, then provides examples. Every sentence adds value, no wasted words. It is appropriately sized for 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?

Given no output schema, the description explains the general nature of output ('curated, ranked results with explanations'), but lacks specifics on the structure of results (e.g., list of tool objects with scores). However, with only 2 parameters, high schema coverage, and clear examples, it is mostly complete for an AI agent to understand and invoke 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 description coverage is 100% (all parameters have descriptions in input schema). The description adds example usage and context but does not add new parameter-level details beyond the schema. Baseline is 3 for high coverage, and the description does not significantly enhance parameter understanding beyond what 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 the tool provides 'AI-powered tool recommendations for a specific need' and is 'the recommended starting point'. It distinguishes from siblings by noting it handles search, filtering, scoring, and ranking in one call, and explicitly suggests using it instead of chaining other tools like search_listings + get_listing + compare_listings.

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

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

Explicit guidance is given: 'Use this instead of chaining search_listings + get_listing + compare_listings', making it clear when to use this tool vs alternatives. The description also implies usage for natural language queries with specific examples.

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 convey behavioral traits. It describes the core search function but omits details like sorting, pagination (only limit, no offset), and response structure. It is adequate but not rich.

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 usage guidelines. No wasted words; every sentence earns its place.

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

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

Given 9 parameters with full schema descriptions and no output schema, the description provides sufficient context for a search tool, including usage differentiation. Lacks details on response format and pagination, but overall complete.

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

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

With 100% schema description coverage, the baseline is 3. The description adds minimal extra meaning beyond the schema; it mentions 'optional filters' but does not elaborate on parameter specifics.

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 'Search for agentic AI tools by keyword query with optional filters,' using specific verb+resource. It distinguishes itself from siblings by specifying when to use semantic_search and get_category.

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 provides when-to-use: 'Use this for keyword-based search.' And when-not-to-use: 'For natural language queries...use semantic_search instead. For browsing all tools in a category, use get_category instead.' This is excellent 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?

No annotations are present, so the description carries the full burden. It discloses that the tool uses AI-powered semantic matching and supports structured filters, but does not describe behavioral aspects such as whether results are scored or ranked, how empty results are handled, or if it is read-only (inferred but not explicit). The description provides adequate but not comprehensive behavioral context.

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: first states purpose, second gives an example query, third lists alternatives. Every sentence serves a clear role without fluff. Highly efficient and front-loaded with the most important 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 9 parameters (all documented in schema) and no output schema, the description provides a solid overview of purpose, usage, and alternatives. However, it omits details about the return format (e.g., list of tools with scores). This is a minor gap for an otherwise well-rounded description.

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 baseline is 3. The description adds value by stating 'Supports structured filters to narrow results (e.g., openSource + deploymentModel)', which implies combinable filters and provides an example. This extra context justifies a score above 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 clearly states 'Search for AI tools using natural language with AI-powered semantic matching', identifying the verb (search) and resource (AI tools). It distinguishes from siblings by explicitly naming alternatives for exact/keyword searches (search_listings) and comparisons (compare_listings).

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 ('Best for conceptual queries') and when-not-to-use with specific alternative tools named ('For exact name/keyword searches, use search_listings instead. For comparing specific tools, use compare_listings.'). This leaves no ambiguity about appropriate contexts.

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