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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that it fetches profiles from the board and returns a verdict with specific metrics, 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 3-4 sentences, front-loaded with the main action, and every sentence adds value. No unnecessary 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 existence of an output schema (not shown) and full parameter coverage, the description fully explains the tool's behavior, return values, and usage context.

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

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

Schema coverage is 100%, with each parameter described. The description adds that both codenames must exist on the board, which is extra context not in 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 specifies a verb ('Compares') and resource ('two operators') and explicitly lists the returned fields (yield, leverage, velocity, class, rank, verdict). It distinguishes from sibling tools like 'get_best_operator' by focusing on side-by-side 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 provides explicit use cases: 'when users ask compare operator X vs Y' or 'who is more efficient' etc. It does not explicitly mention when NOT to use, but the intent 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?

The description discloses the read-only nature (returns assessment without modification) and explains both input modes (codename fetches from board, text computes locally). Annotations already provide readOnlyHint=true, but the description adds details on what is returned and the fact that no data is mutated.

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 at 5 sentences, front-loaded with the primary purpose. Every sentence adds value: purpose, input modes, return values, usage examples. 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 tool's moderate complexity (two optional parameters, output schema present, multiple return fields), the description covers all necessary aspects: what it does, when to use it, how inputs work, what outputs to expect. It references siblings indirectly by its specific focus.

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 both parameters. The description adds value by clarifying that 'text' is for 'raw token pillars' with examples like 'ccusage JSON' and notes case-insensitivity for 'codename'. This goes beyond the schema descriptions, earning a 4.

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 an operator's metrics against board averages and power-user archetypes, returning a behavioral assessment. It also lists specific return values. This distinguishes it from siblings like 'compare_operators' (compares two operators) and 'describe_power_user' (describes characteristics).

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 specifies when to use the tool: when users ask 'how do I measure up to other AI users?', 'am I a power user?', or 'compare me to others'. It also mentions the intent is COMPARE_SELF, providing clear context for invocation.

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?

readOnlyHint=true and idempotentHint=true align with the description's 'static explanatory response' claim, so no contradiction. The description adds only marginal behavioral context beyond the annotations (static nature, domain anchoring) and does not disclose error/edge behavior, but with annotations covering the safety profile the lower burden is met.

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?

Front-loaded with verb+resource and explicit use-case triggers in two sentences. The enumeration of 8 experience tiers with 3 sub-stages is dense, but for a zero-param tool with no output schema it conveys what the agent will receive without being padded.

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 zero-parameter informational tool with matching annotations, the description covers what it returns, what topics it addresses, and when to use it. The tier/operator detail substitutes for the absent output schema; only explicit sibling routing 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 tool takes zero parameters and the description explicitly states it 'takes no parameters', so an agent knows nothing needs to be supplied. With 0 params, the baseline of 4 applies and the description adds a small confirmation of static behavior.

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?

States a specific verb and resource ('Returns an explanatory description of what makes an AI power user') and anchors the content in SigRank's metrics and operator classes. The explain-vs-find semantic clearly differentiates it from siblings search_power_users and get_power_users, though neither sibling is named explicitly.

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 trigger queries ('what is an AI power user?', 'what makes a good AI user?') and an intent label (DESCRIBE_POWER_USER, Informational), giving clear context for when to invoke. It does not state when not to use it or point to alternatives like search_power_users, so it falls short of full routing 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?

The annotations already declare readOnlyHint=true and openWorldHint=true, which covers the safety profile. The description adds meaningful behavioral detail: mentors are 1-2 class tiers above with similar cascade shapes, peers are same-tier ranked by yield proximity, and complementary operators target weaknesses. This goes beyond what the annotations convey.

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

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

The description is moderately long but well-structured with numbered categories and concrete examples. The 'Intent: DISCOVER_PEERS' line and the phrase 'Pass a codename' are slightly redundant with the name and schema, but overall every section 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?

The combination is complete: the schema explains all parameters, the annotations declare read-only and open-world behavior, the description explains the three output categories and ranking rationale, and an output schema exists so return-value documentation is not needed. An agent has enough information to select and invoke this 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%, and each parameter already has a clear description including defaults, enum options, and case-insensitivity. The tool description adds context about what the results mean per category, but it does not add meaningfully new information about how to fill the parameters themselves. Baseline 3 is appropriate given full schema coverage.

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: 'Discovers mentors, peers, and complementary operators for any operator on the SigRank leaderboard.' It clearly distinguishes itself from sibling tools like compare_operators and get_best_operator by describing the three output categories rather than generic comparison or 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 gives explicit trigger phrases: 'who should I learn from?', 'find me a mentor on the leaderboard', and 'who are my peers?', which tells an agent when to use this tool. It does not explicitly state when not to use it or name alternative sibling tools, so it falls just short of 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds meaningful behavioral context beyond the annotations: the tool does not just return raw rankings but adds 'plain-language interpretation of each top operator's cascade' in terms of cache reuse, input economy, and output productivity. No contradiction with annotations (a read operation). It omits edge-case behavior but the annotation coverage lowers the bar.

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 core purpose is front-loaded in the first sentence. The trigger examples and intent label earn their place. The tradeoff is some redundancy: the nine metric names and platform list are duplicated from the schema, slightly lengthening the description without adding new information. Efficient overall, with minor bloat.

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?

An output schema exists, so return values need no explanation. All three optional parameters are documented in the schema with defaults and constraints. The description covers what the tool returns, how it behaves (sort, filter, interpret), and when to invoke it. For a read-only leaderboard query of this complexity, nothing an agent needs to call it correctly 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?

Schema description coverage is 100%, and the schema descriptions are already rich: n has min/max/default, metric includes per-metric semantics ('yield = best overall, velocity = most output per token...'), and platform lists all enum values with default. The tool description enumerates the metrics and platforms but adds no meaning beyond the schema. Baseline 3 applies since the schema does the heavy lifting.

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 opens with a specific verb+resource: 'Returns the top N operators on the SigRank leaderboard'. The scope is precisely defined (top N, sortable by 9 named metrics, optionally platform-filtered), and this clearly differentiates it from siblings like compare_operators (comparison), discover_peers (peer discovery), and compare_self (self-comparison). The embedded 'Intent: BEST_OPERATOR' label and example user queries reinforce what the tool is for.

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 trigger phrases: 'who is the best AI user?', 'who tops the SigRank leaderboard?', 'who has the best leverage?', 'cheapest tokens?', 'show me the AI user leaderboard'. This gives strong when-to-use guidance. However, it does not state when not to use it or route to alternatives (e.g., compare_operators for direct two-user comparison), so it stops short of a full 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?

Annotations already signal read-only and idempotent behavior. The description adds valuable context beyond that: the prompt rotates daily across 9 canonical token metrics, and the response includes the current leader, metric formula, and a ranking link. This helps the agent understand that output is stable within a day but changes daily, which is non-obvious from annotations alone.

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 with the core action front-loaded, followed by useful compositional details and explicit usage triggers. It is slightly verbose with the 'Intent: PROMPT_OF_THE_DAY' tag and the parenthetical SEO/AEO/GEO elaboration, but every sentence contributes to agent understanding. It earns a 4, not a 5, due to minor 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 has no parameters and an output schema exists, the description fully covers what an agent needs: what the prompt is, what fields will be returned (leader, metric formula, link), the daily rotation behavior, and when to use it. No critical information is missing for correct 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?

The tool has zero parameters and the schema description covers this fully (100% coverage). The description appropriately does not discuss parameters because none exist. Per the baseline for zero-parameter tools, a score of 4 is warranted; there is no parameter information to add.

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: 'Returns today's featured prompt from the SigRank prompt registry.' It clearly identifies what the tool returns and distinguishes it from sibling tools focused on operators, comparisons, and efficiency. The additional detail about the prompt being an SEO/AEO/GEO query further clarifies the unique 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 explicitly provides triggering conditions: 'Use this when users ask ...' followed by three concrete example queries. This gives clear context for when to invoke the tool, though it does not mention exclusions or alternatives. Since there are no obvious alternatives for this specific functionality, the lack of exclusions is acceptable.

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 declare readOnlyHint and idempotentHint, indicating safe read operations. The description adds behavioral details: it either fetches from board (codename) or computes locally (text), and returns metrics, suggestions, and references. 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?

The description is moderately sized (4 sentences) and well-structured: it starts with the core purpose, then explains input options, then lists outputs, and finally gives usage examples. Every sentence adds value; 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?

Given the tool has two optional parameters (one of each required), an output schema (present), and annotations, the description covers inputs, outputs, and use cases. It mentions what is returned (metrics, suggestions, references). Minor gap: behavior when both parameters are provided is not specified, but overall it is sufficiently 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 description coverage is 100% (both parameters have descriptions). The tool description adds meaning beyond the schema: it clarifies that codename fetches from the leaderboard while text computes locally, and gives examples of acceptable text formats (ccusage JSON or specific strings). This enriches the semantic 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?

The description clearly states it returns actionable suggestions for improving token cascade efficiency, tied to current metrics. It explicitly differentiates from siblings by naming specific queries and mentioning the two input modes (codename or raw token pillars). The verb 'returns' and resource 'suggestions for efficiency' are specific and unique.

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 includes explicit use-case examples like 'when users ask 'how can I use AI more efficiently?'' and explains the two input options (codename vs. text). It also notes that at least one is required. While it doesn't list when not to use it, the sibling tool names imply alternatives for comparison or description tasks.

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