AI Monk Index

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed world), so the bar is lower, and the description still adds real interpretive caveats: illustrated channels are excluded, pending candidates inflate directory counts, and AI status counts do not constitute reviewed avatar evidence. Only the meaning of 'snapshot methodology' is left unexplained.

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?

Four short sentences, front-loaded with the return payload, each carrying a distinct caveat. Mild deduction for the cryptic 'Metadata separates the full research archive from directory coverage', which is not obvious on first read.

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 no parameters, rich annotations, and an output schema covering the return shape, the description only needs to prevent misinterpretation of the numbers — which it largely does. The unexplained 'metadata' field reference is the one loose end for an agent reading results.

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, so the baseline is 4; the description correctly spends no words on arguments and instead clarifies what the counts mean.

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?

Specific verb and resource: returns directory record/source counts, AI status counts, and snapshot methodology. It is clearly a coverage/statistics tool, distinguishable in spirit from the sibling retrieval tools (get_ai_monk, search_ai_monks), though no 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?

There is no statement of when to call this versus get_ai_monk or search_ai_monks — e.g., 'use to gauge coverage before searching'. The remaining sentences describe how to interpret counts, not when to invoke the tool.

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 establish that this is a safe, read-only, idempotent operation. The description adds meaningful behavioral context beyond annotations by disclosing that pending candidates are included with directory_scope, identity conflicts and unknowns, that illustrated channels are excluded, and that candidate inclusion does not confirm AI production or video format.

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

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

The description is front-loaded with the core retrieval purpose and then adds scoping and caveat sentences. It is compact and mostly efficient, though the second and third sentences are dense with domain-specific terms.

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-value details need not be fully restated, and annotations cover the safety profile. The description is complete enough for calling the tool, but it omits how to obtain the required ID and what happens if the profile is not found.

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?

There is one parameter, id, and the schema provides 0% description coverage. The description partially compensates by clarifying that id means an exact directory profile ID, but it does not specify format, source, case sensitivity, or how to look up the ID.

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, "Retrieve," and names the resource precisely: one exact directory profile ID with its research record and referenced sources. It implicitly distinguishes this lookup-by-ID tool from search-oriented siblings, but it does not explicitly name search_ai_monks or other 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?

The phrase "one exact directory profile ID" implies this tool should be used when an exact ID is already known, but the description never explicitly states when to use it versus search_ai_monks or how to obtain the ID. Usage is therefore implied rather than clearly guided.

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 establish read-only, idempotent, non-destructive, closed-world behavior, so the description's real contribution is the interpretive caveats: 'Prices are not orders; claims are unaudited.' That tells the agent how to treat the numbers, which structured fields do not convey. It stops short of noting any staleness, coverage limits, or empty-result behavior.

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?

Leads with the returned data, then the optional parameter, then the two caveat clauses — front-loaded and free of filler. The first sentence is a dense parenthetical list that takes effort to parse, but every element 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?

With an output schema present and full annotation coverage, the description does not need to explain return shapes; it still helpfully categorizes them and flags the trust caveats. The remaining shortfall is the missing account_id semantics and the absent routing guidance against sibling monetization/analysis tools.

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 0%, so the description must carry the parameter; it only says 'Optional account_id' without stating whose account identifier it is, its expected format, or what the response looks like when it is omitted. Optionality is already implied by the schema's empty required list, so this adds little beyond a restatement.

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 resource and enumerates exactly what is returned (offers, localized presentment observations, seller counters, revenue claims/observations, counts by offer type) for AI monk accounts. That is far more specific than the title alone and clearly separates it from get_ai_monk/search_ai_monks, though it never names a sibling to reinforce the distinction.

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?

There is no statement of when to call this versus get_ai_monk, ai_monk_index_stats, or winning_playbooks, and no guidance on what changes when account_id is supplied versus omitted. The audience must infer the intended use (per-account or aggregate monetization view) entirely from the tool name.

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 cover read-only, idempotent, non-destructive safety, so the description earns credit for going further: pagination, the default sort ordering, how views and revenue sorts are derived (sampled-video views, reported three-month USD gross), and the important 'missing metrics are not zero' data-quality warning. It also discloses the return envelope (provenance, directory_scope, unknowns) and a caveat that candidate inclusion does not confirm AI production. Only rate limits and the meaning of 'status' are left undisclosed.

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 tight sentences, front-loaded with the verb and resource, then ordering behavior, then data-quality caveats. Every clause carries information; nothing is redundant with the schema or annotations.

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 present, return-value explanation is optional, and the description nonetheless signals the key caveats (missing metrics, unknowns, candidate ambiguity). For an 8-parameter tool with zero schema descriptions, the only thing an agent still lacks is the semantics of the status filter and limit/offset behavior.

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 0% across 8 parameters, so the description carries the burden and largely does: it explains what query matches (name/handle, provenance), the audience/views/revenue sort semantics and default, and that language, platform and region are filters. It does not explain the status enum (confirmed/reported/unverified/unknown) or limit/offset bounds, so a real gap remains.

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 (search) and resource (monk profiles and pending research candidates) with the filterable dimensions enumerated. It is clearly a list/search tool versus the single-record sibling get_ai_monk, though it never names a sibling explicitly. The exclusion note ('Excludes illustrated/watercolor storytelling channels') sharpens scope further.

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?

Usage is implied through the filter list and the caveat about candidate inclusion, so an agent can infer this is the discovery entry point before calling get_ai_monk. However there is no explicit 'use this when / use X instead' routing against the six sibling tools. The exclusion clause is the only hard boundary stated.

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 readOnly, idempotent, non-destructive and closed-world, so the bar is lower, and the description still adds real context: ranking is by 'latest dated public views' and 'Views are sampled-video observations, not channel totals', which prevents a serious misinterpretation of the numbers.

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 front-loaded sentences with almost no filler; the ranking criterion and the sampling caveat are stated before detail. The enumeration of return fields is slightly redundant given an output schema exists, but it is compact.

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?

Return values are effectively covered (output schema exists and the description previews fields), and the sampling caveat is a strong addition. Gaps remain around the limit parameter and when this tool beats search_ai_monks or winning_playbooks.

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 0%, so the description must carry parameter meaning. It clarifies that account_id is optional but says nothing about the limit parameter's purpose, default of 10, or maximum of 50, leaving half the params undocumented anywhere.

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 resource (AI monk video samples) and a clear ranking criterion (latest dated public views), plus the payload fields returned. It is distinguishable from siblings like search_ai_monks and winning_playbooks by its ranking/observation framing, though it never explicitly contrasts with them.

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?

There is no when-to-use or when-not-to-use guidance and no named alternative among the five siblings. The only routing hint is 'Optional account_id', which implies a global-vs-account-scoped choice but leaves the agent to infer it.

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 readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: results are 'source-linked' (provenance is attached) and 'creator claims are unaudited' (data reliability caveat), which an agent needs before relaying these numbers. It does not address result size or pagination behavior.

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?

A single dense sentence that front-loads the resource and its provenance, then lists the payload fields, closing with the reliability caveat. Every clause carries information; no restatement of the title or name.

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 structure need not be explained, and the description covers content scope plus the unaudited-claims caveat. The one remaining gap is whether this returns the full catalog at once or a bounded/paginated subset, which matters for a no-param collection 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 takes zero parameters, so per the rubric the baseline is 4 — there is nothing for the description to disambiguate. The description correctly implies a no-argument bulk retrieval rather than a filtered query.

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

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

The description states a specific resource — source-linked playbooks from successful AI monk creators — and enumerates the contents (script formula, hooks with views, voice, cadence, funnel, monetization), which lets an agent distinguish it from get_ai_monk or search_ai_monks. It never explicitly contrasts itself with those siblings, so the differentiation is implicit rather than stated.

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?

There is no when-to-use guidance and no alternatives named, despite four siblings (search_ai_monks, get_ai_monk, top_formats, how_they_monetize) that plausibly overlap. The agent must infer from the noun 'playbooks' alone that this is the curated-collection entry point rather than a search or lookup tool.

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