Straight Line Golf Pro

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context by stating the output is mechanical observations and explicitly excluding factual or ranking validation, which an agent could not infer from the annotations or schema 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 entire description is one well-structured sentence with the main action front-loaded and a clarifying scope clause after the semicolon. There is no redundancy or filler; every phrase 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?

For a low-complexity, single-parameter tool with helpful annotations, the description covers what is inspected and explicitly sets expectations about what it does not do. It does not describe the exact output shape, but the lack of an output schema is less critical given the simple observational purpose.

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%, and the description does not explicitly document the url parameter. However, with a single required string url and the description naming the signals the tool inspects, the meaning is reasonably inferable; it just does not fully compensate for the absent schema descriptions.

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 names a specific resource ('canonical, title, H1, schema and crawler signals') and a specific action ('inspect'), making the tool's purpose clear. It distinguishes itself by saying it is 'not factual or ranking validation', but it does not explicitly contrast itself with sibling tools like fetch or discovery_status.

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 'mechanical observations, not factual or ranking validation' implies when not to use the tool, and the listed signals imply when it is appropriate. However, it never names alternatives or gives explicit conditions such as 'use discovery_status for indexing status' or 'use fetch for raw page content', leaving usage guidance mostly implicit.

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 report readOnlyHint and destructiveHint, so the description appropriately avoids repeating that. It adds meaningful behavioral context: the tool reports live sitemap counts and declared policies, and explicitly disclaims actual crawler visits. This clarifies what the agent should not infer from the results.

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 short sentences, front-loading the main action in the first word and adding a crucial caveat in the second sentence. Every word earns its place, with no filler or redundant restatement of the tool 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?

For a zero-parameter read-only status tool, the description covers what is read and what it does not claim. It is slightly light on describing the exact return structure, but the lack of an output schema is offset by the simplicity of the tool's purpose and the clear annotations.

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 fixes 0, so there is no parameter semantics burden on the description. The schema already covers this fully, and the description's content about what is being read is not needed for parameter disambiguation.

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 ('Read') and names the exact resource ('live sitemap counts and declared search/AI crawler policies'). It also explicitly disclaims actual crawler visits, which distinguishes it from what could otherwise be confused with a crawler-analytics tool.

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 'Does not claim actual crawler visits' clause provides a clear when-not-to-use boundary, implying this tool is for declared policies rather than measured visits. However, it does not name sibling tools or explicitly state when to prefer discovery_status over them, so it falls just short of full 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?

Annotations already establish read-only, non-destructive, open-world behavior. The description adds useful behavioral context by disclosing the return shape ('verified current public text and citation URL') and emphasizing that the content is current. No contradiction with annotations exists.

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, well-structured sentence front-loads the action and resource, then states the output. Every phrase earns its place, and there is no redundant or vague filler.

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 read tool with readOnly and openWorld annotations, the description covers the source of the id and the return value. It does not explain failure modes or URL format details, but the tool's low complexity means the description is largely sufficient 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 coverage is 0% and the only parameter is an undocumented 'id'. The description compensates by linking that id to the canonical public URL returned by search or list_public_pages, giving the agent a concrete way to obtain and understand the parameter. It stops short of fully explaining the expected URL format, but is sufficient for a one-parameter tool.

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 action ('Fetch'), a specific resource ('canonical public URL returned by search or list_public_pages'), and the expected output ('verified current public text and citation URL'). It clearly distinguishes this tool from siblings like search and list_public_pages by positioning fetch as the follow-up retrieval step.

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 clear context: the canonical URL must come from search or list_public_pages, which tells an agent when fetch is appropriate. It does not explicitly list exclusions or alternatives, but the source constraint effectively routes the tool relative to 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 cover read-only, open-world, and non-destructive behavior. The description adds materially useful behavior beyond annotations: the list reflects the live sitemap, pagination uses next_offset, and each returned URL still needs independent fetch validation. This sets accurate expectations without contradicting 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?

Two tightly packed sentences with no filler. The core action and resource are front-loaded, the pagination detail is immediately useful, and the critical validation caveat is placed at the end without bloating the description.

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 two-parameter list operation, the description covers the key aspects: what is listed, how pagination works, and the necessary follow-up fetch step. There is no output schema, so a more explicit response shape would be slightly better, but 'List ... URLs' and 'paginate with next_offset' give enough 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, and it partially does by introducing next_offset as a pagination mechanism that implies how offset should be used. However, it does not explicitly explain the limit/offset relationship or specify bounds; the generic parameter names and defaults carry most of the semantic weight.

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 a concrete action ('List') and a specific resource ('live sitemap public URLs'), which distinguishes this enumeration operation from the sibling fetch, search, and discovery tools. The pagination note reinforces that the tool returns an iterable list of URLs.

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 gives an explicit workflow cue: 'Each page still requires fetch validation' tells the agent that this tool only lists candidates and that successful listing does not imply valid pages. It does not explicitly contrast with search or discovery_status, but the fetch workflow is the key alternative relationship and is clearly 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?

Beyond the read-only and non-destructive annotations, the description discloses a non-obvious behavior: matching happens against sitemap URL paths, not page content, and results should be verified via Fetch. This adds meaningful behavioral context without contradicting 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?

Two concise sentences, front-loaded with the primary purpose and immediately clarifying the non-full-text constraint. The second sentence adds actionable follow-up guidance, and every phrase 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?

For a simple one-parameter, read-only tool with no output schema, the description covers what is searched, what is not searched, and the recommended verification step. It does not describe the result shape or pagination, but those are lower risk given the open-world and read-only annotations.

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 a bare 'query' string, so the description must supply semantics. It does: query terms are words matched against sitemap URL paths, not full-text. It stops short of specifying exact syntax (single vs. multiple words, case sensitivity, substring behavior), but for one required parameter this is adequate.

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 ('Find public SLGP pages') and defines the exact matching scope ('words in sitemap URL paths'), explicitly ruling out full-text search. This distinguishes it from a generic search tool and from sibling tools like list_public_pages, which likely list pages without path-word filtering.

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 clear context for when to use search: when the goal is to locate public pages by words in URL paths, not full-text content. It also instructs to use Fetch to verify titles and content, which is an explicit workflow cue, though it does not enumerate alternatives like list_public_pages for unfiltered listing.

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