AgentCrush

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds that the tool returns scoring data and comparison context, and emphasizes evidence-based comparison (no universal winner). This complements annotations without contradiction.

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 that efficiently convey purpose, usage, and behavioral insight. 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?

Given the tool's low complexity (single parameter with full schema coverage and an output schema), the description adequately covers purpose, usage, and behavioral context. It is complete 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 coverage is 100% with a clear description for the handles parameter (array of 2-5 strings). The description reiterates the count but adds little beyond 'side-by-side' context. Following the guidelines, 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 the tool compares 2-5 AI agents side-by-side across categories, returning scoring data and comparison context. It uses specific verbs and resources, and it distinguishes itself from siblings like get_agent_details.

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 for "X vs Y" queries', providing clear guidance on when to invoke the tool. It also notes that AgentCrush does not declare a universal winner, adding behavioral context. It lacks explicit exclusion of alternatives but is still strong.

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?

Annotations already indicate readOnlyHint, idempotentHint, and no destructiveness. The description adds value by explaining the mechanism (pairwise delta scan) and listing the specific fields monitored for changes, going beyond the annotation signals.

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-loading the core action and then providing specific details. Every word earns its place with no redundancy or vagueness.

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?

With an output schema, annotations, and complete parameter schema, the description covers the essential aspects of a change-reporting tool. It explains the delta concept and listed fields, though it does not clarify what constitutes 'recent' (default 7 days from schema) or export constraints.

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 coverage is 100% with descriptions for each parameter (handle, limit, since). The description does not add additional parameter context beyond what the schema provides, so the baseline 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?

The description clearly states the tool performs a 'pairwise delta scan over an agent's recent snapshots' and reports 'material changes in score, rank, github_stars, follower_count, identity_type, etc.' This specific verb-resource combination distinguishes it from siblings like get_agent_details (full details) or compare_agents (cross-agent comparison).

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 lacks explicit guidance on when to use this tool versus alternatives. While it mentions being a 'mirror of GET /api/agent/{handle}/changes,' this implies a REST-equivalent usage but does not state exclusions or direct comparisons to 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?

Annotations already mark it as read-only and idempotent; the description adds the valuable context of returning fuzzy-match suggestions and the specific data categories, going beyond annotations without contradiction.

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 the main purpose and returns details, without unnecessary verbiage; it is efficient but could be slightly more structured.

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

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

Given the single parameter and existing output schema, the description adequately covers the tool's behavior, including the important fuzzy-match fallback, making it complete for its 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?

With 100% schema coverage and a well-described 'handle' parameter, the description adds the notable fuzzy-match behavior detail, which enhances the 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 states the specific verb 'Get full details for a specific AI agent' and lists the included data components, clearly distinguishing it from siblings like 'search_agents' or 'compare_agents'.

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

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

The description explains when to use the tool (to get full details for a specific agent) and provides guidance on handling fuzzy-match suggestions, but does not explicitly state when not to use it or mention 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?

Description adds behavioral details beyond annotations: daily snapshots, deduplication per calendar day, and trend summary. Annotations already indicate read-only, idempotent, non-destructive, and open-world, so the description enriches understanding without contradiction.

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 efficiently convey purpose and key details with no extraneous words. Front-loaded with verb and resource.

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

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

Given the presence of an output schema and rich annotations, the description adequately covers deduplication, time range, and return summary. No missing critical information for agent selection or 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?

Input schema has 100% coverage with descriptions for both parameters. The description briefly mentions the date range ('past 1–90 days') but adds no new semantic meaning beyond the schema. 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 the tool retrieves rank and score history for an AI agent over a specified date range, with specific verb and resource. It distinguishes itself from siblings like get_agent_details (which likely provides a snapshot) and compare_agents (comparison) by focusing on historical trend.

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 phrase 'Useful for showing how an agent's standing has evolved' provides a clear use case. However, it does not explicitly state when to avoid this tool or mention alternatives, leaving some guidance implicit.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by explaining that the score combines multiple components (confidence_tier, evidence tier, ERC-8004 identity, risk flags) and is a mirror of a GET API, providing behavioral context beyond the annotations.

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

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

The description is two sentences, front-loading the purpose and then detailing components and API mirror. Every sentence is informative without redundancy, making it highly concise.

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 one-parameter tool with complete annotations and an existing output schema, the description adequately covers the purpose, output format, and behavior. It mentions the classification categories and underlying components, making it complete for the tool's 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?

The single parameter 'handle' is fully described in the schema with 'Agent handle slug.' With 100% schema coverage, the description adds 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 a composite trust score (0-100) and classification for delegation decisions, specifying the verb 'get' and the resource. It distinguishes itself from sibling tools like get_agent_details by focusing specifically on trust metrics.

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 indicates the tool is for delegation decisions but does not provide explicit guidance on when to use it versus alternatives like get_agent_details or compare_agents. Usage context is implied but not fully clarified.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds beyond by specifying return structure (ordered by composite score with sub-scores) and default filtering. No contradiction with annotations.

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

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

Two sentences, front-loaded with purpose, second sentence adds key detail. Every sentence earns its place with no 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?

Given the tool's simplicity (3 params, output schema present), the description is fully adequate. It covers purpose, return structure, and default behavior without needing to explain return values due to 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 coverage is 100% with descriptions for all 3 parameters. Description adds value by stating default for evidence_ready_only, though a minor inconsistency exists in the enum count (description says '5', parameter description says '4') but that's a schema issue not in the tool description.

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 verb 'Get', resource 'ranking for one of 5 categories', and specifics like composite score and sub-scores. It distinguishes from sibling tools like get_agent_details or compare_agents.

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?

Description implies usage for ranking and mentions default behavior ('Defaults to evidence-ranked only'), but does not explicitly state when to use this tool vs alternatives or provide 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds value by specifying the exact data returned (counts, categories, leaders, volume) and confirming it's a mirror of an API endpoint, providing behavioral context beyond annotations.

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

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

The description is two sentences with no wasted words. The first sentence front-loads the key purpose and data points, while the second briefly mentions the API mirror. 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 no parameters, strong annotations, and an existing output schema, the description provides sufficient context about what the tool returns. It includes details about counts, categories, leaders, and volume, making it complete for a parameterless summary 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 tool has no parameters, and schema coverage is 100%. According to guidelines, 0 parameters sets a baseline of 4. No additional parameter explanation is needed.

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 an ecosystem-level summary with specific data points: counts, category mix, leaders, and snapshot volume. It distinguishes itself from sibling tools like get_agent_details and get_agent_changes, which are agent-specific.

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 this is a top-level overview via 'one-call ecosystem summary,' but does not explicitly state when to use it versus alternatives or provide exclusion criteria. Sibling names offer context, but the description itself lacks direct 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context about the return contents (weights, formulas) and the behavior that 'methodology travels with data' (static across calls). No contradictions. The extra behavioral detail justifies a 4.

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: first states purpose concisely; second provides usage context. No unnecessary words or repetition. Highly 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 the tool has only one parameter, a clear output schema (as per context), and the description lists what the methodology contains, it is fully adequate. The usage guidance rounds it out.

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 well-described enum parameter. The description adds no additional semantic meaning beyond 'one category.' With high schema coverage, baseline is 3, and the description meets that but does not surpass it.

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 'scoring methodology for one category' with specific items like weights and formulas, using a specific verb 'Get' and resource 'scoring methodology.' It distinguishes from siblings like get_category_ranking which retrieves rankings, not methodology.

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: call this 'when explaining HOW a ranking works so the LLM can give a methodology-accurate answer instead of guessing.' This tells the agent when to use this tool and implicitly not to use it for retrieving plain rankings.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds specific behavioral context by naming the exact protocols/surfaces covered and stating it returns counts per protocol. This goes beyond annotations, though it could mention that values are aggregated and not filterable.

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, efficiently front-loading the action ('How many indexed agents touch each major protocol/surface') and supporting with a usage hint. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (no parameters, clear output schema present), the description fully captures what the tool does and when to use it. It provides enough context for an AI 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?

The tool has zero parameters and schema coverage is 100% (empty schema). The description adds value by explaining what the output represents (counts per protocol), which is sufficient since no parameters need 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 tool's purpose: getting counts of how many indexed agents use each major protocol/surface. It lists specific protocols (ERC-8004 verified, Virtuals tokens, etc.), which provides concrete scope and distinguishes it from sibling tools that might provide different aggregate views.

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 indicates it is 'useful for ecosystem-state questions', which gives a clear usage context. While it doesn't explicitly state when not to use it, the sibling list (e.g., get_ecosystem_summary) implies alternatives, and the scope is well-defined.

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 annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false appropriately, covering safety. The description adds useful context: it computes from agents.weekly_delta and returns weekly movers, which helps the agent understand the data source and time frame. No behavioral contradictions.

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, highly concise and front-loaded. The first sentence states the core function, the second adds usage context and default. Every sentence is necessary; no wasted words.

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

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

Given the output schema exists and annotations cover safety, the description adequately explains the tool's data source, purpose, and default behavior. It is complete for a simple read-only query tool with well-documented 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?

All three parameters have complete schema descriptions (100% coverage), so the schema already explains their meaning. The description only repeats the default limit (already in schema), adding no new semantic value beyond what the schema provides. Baseline score applied.

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 weekly rank movers from agents.weekly_delta, specifying it includes both up and down movements. This verb+resource combination is specific and distinguishes it from siblings like get_agent_changes or get_agent_history, which focus on different aspects of agent data.

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 it is useful for surfacing notable changes since last week, providing clear usage context. It also mentions the default limit of 10 per direction. However, it does not explicitly state when not to use the tool or name alternatives, though the sibling context implies different 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool returns exactly 4 categories with specific data, which is consistent and provides additional context about the output.

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 wasted words. It front-loads the action and result, making it easy for an agent to parse 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 zero parameters, comprehensive annotations, and an output schema, the description provides all necessary context. It tells the agent what the tool returns and when to use it, with no gaps.

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 tool has zero parameters and schema coverage is 100%. The description adds no extra parameter info, but none is needed. Baseline for zero parameters is 4, but the description also explains the output, making it a perfect fit.

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 specific verbs ('List') and resources ('AgentCrush agent categories') and includes details ('tracked + evidence-ranked counts and current methodology versions'). It clearly distinguishes this tool from siblings like 'get_category_ranking'.

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: 'Use this for market-level discovery — what kinds of agents does AgentCrush track and how many of each?'. This provides clear context and implies not to use it for detailed ranking or individual agent details.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is clear. The description adds that it returns matching agents with category, tier, and rank info, but does not disclose search behavior like case sensitivity or fuzzy matching. With annotations covering safety, this is adequate.

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

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

Two efficient sentences: first states purpose and return, second covers filters and future compatibility. No fluff or redundancy, front-loaded with key 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 moderate complexity (nested filters, output schema present), the description sufficiently covers what the tool does, how to use it (query and filters), and what it returns. Annotations cover safety, so no gaps remain.

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 mentions using the 'filters' object for constraints, which echoes the schema but adds no new meaning. It does not elaborate on parameter values or behavior 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 the verb 'search' and the resource 'AI agents across AgentCrush's evidence-ranked index'. It specifies return information (category, tier, rank), distinguishing it from sibling tools that retrieve specific agent details or compare agents.

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 that this tool is for broad searching, but it does not explicitly state when to use it versus alternatives like get_agent_details or list_categories. No exclusions or when-not-to-use guidance is provided.

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