guide-registry

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

Annotations already communicate that this is read-only and non-destructive. The description adds that the result includes artifact and routing-surface status, which is useful but not extensive. No hidden destructive or mutating behavior is disclosed beyond what annotations already indicate.

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 compact sentence that states the operation, target, key parameter, and output scope. Every phrase earns its place 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?

For a simple, single-parameter read-only tool with an output schema, the description provides enough context for correct invocation. The safety profile is covered by annotations, and the slug parameter is fully documented in the 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 description coverage is 100% and the single slug parameter is already documented with an example. The description reinforces that slug is the lookup key but does not add substantial new meaning 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 uses a specific verb 'Get' tied to a clear resource ('one guide's registry row') and the lookup method ('by slug'). It also names the key return content ('artifact + routing-surface status'), which distinguishes this tool from list-oriented siblings like list_guides.

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 fetching a single known guide by slug, which is clear context. However, it does not explicitly state when to use this tool instead of list_guides or mention any exclusions or 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?

Annotations already declare the tool read-only and non-destructive, so the bar is lower, but the description adds substantial behavioral nuance: null means 'not yet joined' rather than 'no link', ksao_type='other' is unvalidated, only ~30% of elements are person attributes, and canon_promotion_candidate is a real answer not a failed lookup. These are exactly the hidden semantics an agent needs to interpret results correctly, and they go far 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?

Three dense sentences: one states the purpose and primary modes, one gives the exact query patterns, and one resolves the most dangerous interpretation trap (null semantics). No filler, no repetition of schema text, and the most important guidance is front-loaded.

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 optional parameters, 100% schema coverage, an output schema, and read-only annotations, the description covers the key decision points and behavioral hazards: null handling, validation status, construct-class semantics, and canon promotion. The tool is complex, but nothing an agent needs to invoke it correctly or interpret its results 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?

All 12 parameters are already documented in the schema (100% coverage), so the baseline is 3. The description adds value by front-loading the three most important query modes — min_guides=2, role=<role>, and capability_detail=<slug> — into the lead sentence, making it easy for an agent to pick the right parameter combination without reading every schema entry. That pushes it slightly above baseline, but the schema still does most of the semantic 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?

The description opens with a specific verb and resource: 'Traverse the capability spine joining books, guides, KSAO-grade elements and jobs.' This clearly distinguishes it from sibling tools like list_guides or get_guide by framing it as a cross-entity query tool rather than a simple list or fetch. The title reinforces the same scope, so an agent can tell it apart without opening the schema.

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 concrete scenarios: min_guides=2 for cross-cutting capabilities, role=<role> for job requirements, capability_detail=<slug> for a hydrated package, and construct_class for distinguishing what a role needs from what a capability produces. It does not explicitly say 'use list_guides instead when ...', but the use cases are clear enough that an agent can select this tool confidently without confusing it with its siblings.

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 read-only/non-destructive, so the description needs to add output behavior. It does mention the distribution roll-up and the no-rendering-surface edge case, but it presents full-row fields and roll-up as unconditional while the schema default for fields is 'compact', which omits rollup/registry_meta and several listed fields. That makes the behavior description misleading for default calls.

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 front-loaded sentence with no filler; the verb and object come first. The long dash clause is dense but relevant, though it would be tighter if it noted the fields=full dependency.

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 13 optional filters, full schema coverage, and an output schema, the structured data already provides most context. The main gap is that the description's roll-up/full-row promise conflicts with the default compact row shape, so an agent must read the fields parameter to avoid expecting output the default call will not return.

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 13 parameters are described in the schema, so the baseline is 3. The description does not add parameter-level meaning; it mostly recaps output fields, which is fine because the schema carries the parameter documentation.

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 object, 'List the bicycle.guide registry', and clarifies breadth ('every guide') plus the included fields. This cleanly separates it from singular get_guide and from list_capabilities/list_taxonomy.

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 registry-scope wording implies using this when you need the whole guide list rather than a single guide or another taxonomy. It never names alternatives or exclusions explicitly, so it stops 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 establish the tool as read-only and non-destructive. The description adds useful behavioral context: the tool returns machine-readable structured data and includes live guide counts, which signals both format and dynamic content beyond the basic read-only annotation.

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 dense sentence with no filler. Every clause adds specific information about the returned taxonomy, and the core purpose is front-loaded with 'Get the bicycle.guide vocabulary'.

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 parameterless, read-only tool with an output schema, the description provides sufficient context. It names all major components of the taxonomy and clarifies the data format, so an agent has everything needed to decide when to call it.

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 there are no parameter semantics to document. The description compensates by enumerating what the returned vocabulary contains, making the tool's output scope clear despite having no input schema details.

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 specifies the operation ('Get') and the resource ('bicycle.guide vocabulary'), then enumerates the exact kinds of data included: domains, shelves, series, personas, guide_types, and classification conventions. This makes it easy to distinguish from siblings like list_guides or get_guide.

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 'as machine-readable data rather than prose' gives clear context that this tool is for structured vocabulary retrieval. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for an agent to select it appropriately.

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