Zyrvix Docs

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

No annotations exist, so the description must carry the full burden. It discloses that the full text is returned, but not the behavior for invalid ids or potential errors. The prerequisite to call list_doc_sections mitigates some risk but doesn't fully cover behavioral transparency.

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: the first states the core purpose, the second provides a direct usage tip. No superfluous words, and the purpose 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?

For a simple one-parameter tool with an output schema present, the description covers the main purpose and the prerequisite to obtain an id. It lacks explicit error-handling info, but the simplicity and output schema reduce the need for more detail.

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 schema has no description for the 'id' parameter (0% coverage). The description adds meaning by stating the id identifies a documentation section and tells the user to obtain valid ids from list_doc_sections(). This is helpful but could specify the id format or type further.

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 the specific verb 'Return' and names the resource 'documentation section' accessed by id. This clearly differentiates it from siblings: list_doc_sections enumerates sections, while search_docs searches content.

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?

It explicitly instructs to call list_doc_sections() first to get valid ids, which provides a clear workflow and prerequisite. It does not explicitly contrast with search_docs, but the purpose is clear enough to guide selection.

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?

No annotations are provided, so the description carries the full burden. It discloses the output shape ('{id, title}') and ordering ('document order'), which is useful. However, it does not mention potential side effects, error behavior, or explicitly state that the operation is read-only, though 'List' strongly implies non-mutating 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?

The description is a single sentence that is front-loaded with the key verb and resource, then provides the output format. There is no wasted text 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?

For a simple list tool with no parameters and an existing output schema, the description provides the essential behavioral detail (document order, output shape). It is complete enough for an agent to invoke the tool successfully, though it could additionally mention when to prefer sibling 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?

The tool has zero parameters, so the description does not need to add parameter-level detail. The absence of parameter information is appropriate and does not create any ambiguity.

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 action ('List'), the resource ('documentation section'), the scope ('every'), and the output format ('{id, title}'). It also implicitly distinguishes itself from sibling tools like 'get_doc_section' (specific section) and 'search_docs' (search).

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 when you need all documentation sections in document order, but it does not explicitly state when to use this tool over alternatives or mention any exclusions. With siblings like 'get_doc_section' and 'search_docs' available, the lack of explicit usage guidance leaves a gap.

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?

With no annotations provided, the description carries the full behavioral burden. It discloses the output shape (section_id, title, snippet), a limit of up to 5 results, and that results are ordered by best match first. This provides meaningful transparency beyond a basic statement, though it does not detail matching algorithms or edge cases.

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: the first directly states the action, the second describes the return output and limit. Every sentence contributes meaning with 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?

For a simple one-parameter tool with an output schema, the description covers purpose, scope, return format, limit, and ordering. It does not mention edge cases like empty results or search semantics, but the provided details are sufficient for typical usage and align well with the tool's simplicity.

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 schema only defines query as a required string without a description. The description adds crucial meaning by stating it accepts a keyword or phrase, compensating for the 0% schema description coverage. It does not elaborate on matching rules (e.g., case sensitivity, partial matches), but offers essential context for the parameter.

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 searches all documentation sections for a keyword or phrase, using a specific verb and resource. It distinguishes itself from siblings get_doc_section and list_doc_sections by focusing on keyword discovery across the entire documentation set.

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 its use when searching across all documentation sections but does not explicitly compare to sibling tools or state when to use it over get_doc_section or list_doc_sections. No exclusions or alternatives are mentioned, leaving usage context somewhat implicit.

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