Scrykeeper Royal Road Intelligence

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral context: 'resolved' indicates identity resolution, 'deduplicated observed audience footprint' signals data processing, and the caveat 'not guaranteed to be a complete bibliography' sets expectations about data completeness. 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 a single front-loaded sentence of about 20 words. It leads with the primary output, lists additional return components, and ends with an important data-quality caveat. No filler or 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?

This is a simple tool with one required integer parameter, a rich output schema, and annotations covering read-only and idempotent behavior. The description names the full return set and flags a data limitation. It is slightly incomplete on how to obtain or interpret identity_id, but for a straightforward retrieval tool that is a minor gap.

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

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

Schema description coverage is 0%, so the description must compensate. It connects identity_id to a 'Scrykeeper author identity' and a 'resolved' status, adding domain context. However, it does not explain where identity_id comes from, its format, or how resolution works, leaving the agent with only the parameter name and a scant hint.

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 verb and resource: 'Get a resolved Scrykeeper author identity, known works and deduplicated observed audience footprint.' This clearly indicates an author-intelligence retrieval tool. However, it does not explicitly contrast with siblings like search_market or get_story, relying on the tool name for differentiation.

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?

No guidance is given on when to use this tool versus the sibling tools. There are no mentions of alternatives, exclusions, prerequisites, or the source of identity_id. Usage is only loosely implied by the purpose, without any decision support.

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 idempotentHint=true, covering the safety and side-effect profile. The description adds value by disclosing the content scope: data coverage, freshness, methodology, and limitations. This goes beyond the annotations without contradicting them, though it does not mention any potential delays or rate limits. With the annotation coverage, this is a solid 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?

The description is two concise sentences. The first sentence states the output scope, and the second provides usage context. Every word earns its place, with no redundancy or fluff. It is front-loaded with the 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?

The tool has no parameters, and an output schema is present (though not shown), so return format is covered. The description explains what the tool returns and when to use it, which is sufficient for an agent to decide when to call it. Given the simplicity of the tool and the annotation coverage, nothing 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 has zero parameters, so the description has no parameter burden. The baseline for 0 params is 4, and the description does not need to explain anything further. Schema coverage is 100% (empty properties), so nothing is missing.

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 starts with a specific verb 'Return' and names the exact resource: 'Scrykeeper's current data coverage, source-specific freshness, methodology and limitations.' This clearly distinguishes it from sibling tools like get_author, get_story, and search_market, which target individual entities or market searches. The purpose is unambiguous and highly 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 explicitly states when to use the tool: 'Use this when interpreting reader, author or commercial metrics.' This provides clear contextual guidance. However, it does not mention when not to use it or name alternative tools, so it falls short of a 5 but is well above the minimum.

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 and idempotentHint, so the description isn't needed for safety signaling. The description adds valuable context beyond annotations: the data-sampling caveat ('Observed readers are sampled graph observations and are not total readership') and access-tier limits with a link. These are genuinely useful behavioral disclosures for an agent relying on the data.

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 short sentences with no filler. The purpose is front-loaded, followed by the data caveat and access information. Each sentence earns its place, and the structure is easy to scan.

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 single-parameter read tool with an output schema and readOnly/idempotent annotations, the description covers the essential bases: purpose, data-fidelity caveat, and access constraints. The only notable omission is guidance on how to discover or validate a fiction ID, which is minor given the tool's simplicity and the presence of an 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 0% for rr_fiction_id, so the description carries the burden of explaining the parameter. The description clarifies that rr_fiction_id is a 'Royal Road fiction ID' and implies single-ID lookup, which adds meaning over the bare integer schema. However, it doesn't explain the ID's format, range, or how to obtain one, leaving a partial gap.

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 verb and resource: 'Get Scrykeeper intelligence for one Royal Road fiction ID.' The phrase 'one ... fiction ID' clarifies the tool's scope as single-story lookups, which helps distinguish it from search_market and get_author, though it doesn't name sibling tools 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?

No guidance is provided about when to use this tool versus alternatives like get_author or search_market. The description offers access-tier information but doesn't state conditions like 'use when you have a specific fiction ID' or 'use search_market to discover IDs.' The agent must infer usage from the purpose alone.

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 and idempotentHint, so the safety profile is known. The description adds valuable behavioral context beyond this: it clarifies the semantics of commercial='none' (not unpublished, but no accepted mapping) and hints that readers data is 'sampled observed', which is not in the schema. 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 a single, information-dense sentence that front-loads the core purpose and tacks on a precise clarification. Every word earns its place; no redundancy or 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?

With 12 parameters and no schema descriptions, the description is only partially complete. It omits guidance on the search query (q), sorting (sort), pagination (limit/cursor), and the meaning of 'sampled observed readers'. The output schema exists, so return-value documentation is not needed, but the missing usage details leave gaps 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?

Schema description coverage is 0%, so the description must compensate. It names most filter dimensions (genre, lifecycle, followers, readers, updated_days, commercial) but leaves q, sort, limit, cursor undocumented and does not explain min/max range semantics. The commercial clarification adds meaning, but coverage is incomplete.

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 verb ('Search') and resource ('Scrykeeper's Royal Road market dataset'), and enumerates the filter dimensions (genre, lifecycle, followers, readers, update recency, commercial mapping). It distinguishes itself from siblings (get_author, get_data_context, get_story) by being a dataset-wide search rather than a single-entity fetch.

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 usage for searching market data but provides no explicit when-to-use vs. alternatives guidance. It does not mention exclusions or scenarios where a sibling tool would be preferred, leaving the agent to infer from the sibling names.

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