hibot

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

There are no annotations, so the description carries the full behavioral burden. It implies an informational/read-only tool through 'Use it to explain...' and the get_ naming, but it never explicitly states what the tool returns or that it has no side effects. The awkward phrase 'The ANSWER framework HiBot audits with' could momentarily suggest an audit action rather than retrieval of methodology content.

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 only two sentences and is largely efficient, but the first sentence is grammatically awkward ('The ANSWER framework HiBot audits with...') and packs a list of six items into one fragment. Despite that, it stays compact and front-loads the key content.

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 informational tool, the description is nearly complete: it lists the methodology components and states the intended use. It does not describe the exact return format, but the content inventory makes the tool's output predictable even without 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?

The input schema has zero properties, so there are no parameters to document; the baseline for zero-parameter tools is 4. The description adds contextual meaning about the referenced methodology, which is sufficient.

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 (the ANSWER methodology) and enumerates its contents: six AI visibility categories, 0-4 response rubric, category weights, score bands, and letter grades. The final sentence adds the use case (explaining how AI visibility scores are measured), which clearly distinguishes this methodology reference from the blog/case-study/plans siblings.

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 it to explain how an AI visibility score is measured.' It does not explicitly name alternatives or exclusion conditions, but the sibling tools are clearly different content types, so the guidance is sufficient for an agent to select this 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It usefully reveals that only published articles are returned and that the output is readable Markdown with a canonical URL, but it does not state error behavior, authentication needs, or read-only guarantees.

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 leads with the key facts: one published article, identified by slug, and the output format. Every word contributes meaningful information with no 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 one-parameter retrieval tool with no output schema, the description covers the most important caller needs: input slug, article status, and output format. It does not cover failure cases or formatting details, but the simple scope makes this a reasonably complete description.

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 sole parameter, slug, is already described in the schema as coming from list_blog_articles or search_blog. The tool description only repeats 'by slug' and adds no extra format, example, or edge-case guidance.

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 tool name and description clearly indicate a single, published HiBot blog article is retrieved by slug and rendered as Markdown with its canonical URL. The phrase 'One published HiBot article' distinguishes it from case studies and from list/search siblings.

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 context is implied rather than explicit: the description says 'by slug' and the parameter schema says the slug comes from list_blog_articles or search_blog, which hints at the workflow. However, there is no direct statement about when to prefer this tool over siblings or when not to use 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?

With no annotations, the description carries the burden of behavioral disclosure. It states that the tool returns a summary rather than the full report and enumerates the three content elements, which is meaningful transparency for a read-style retrieval tool. It does not cover error behavior or authentication, but those are less critical for this simple get operation.

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 states the core action and key first, then uses a colon to list the expected returned components. 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 one-parameter retrieval tool, the description covers what the output includes and the input key. It is slightly incomplete only in not explicitly guiding the agent toward list_case_studies when the slug is unknown, though the schema already points there.

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 100% and the slug parameter is already documented as coming from list_case_studies. The description repeats 'by slug' without adding format, defaults, or edge-case meaning, so it does not raise the baseline.

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 identifies the resource (one HiBot case study) and the key (slug), and specifies what the returned summary contains: headline finding, report coverage, and request page. It differentiates from list_case_studies by the singular focus, but does not explicitly contrast with siblings like get_blog_article.

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 use case is implied: an agent should call this when it has a case-study slug and wants the summary of that one study. The schema's slug description adds the source (list_case_studies), but the description itself gives no explicit when-to-use or alternative routing.

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 must carry the behavioral disclosure burden. It does disclose the information domains covered, including a non-obvious action ('where customers register'), but it does not state return format, whether this is a read-only retrieval, or any limitations. For a zero-parameter informational tool this is acceptable but not richly transparent.

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 focused sentence that front-loads the resource name and immediately enumerates its contents. There is no redundant phrasing or filler; every element adds useful 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?

Given no parameters, no output schema, and a simple informational purpose, the description covers the essential content areas comprehensively. It could marginally improve by explicitly stating the output form, but the enumerated list already communicates what the agent will receive.

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 baseline is 4. The description appropriately focuses on the content of the returned data rather than parameter meaning, which is not needed here.

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 ('HiBot Pulse and Panorama audit plans') and enumerates the exact content it covers: price, question count, AI engines, runs per question, report inclusions, and registration location. This clearly differentiates it from sibling tools focused on blogs, case studies, and methodology.

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 that this tool is for retrieving audit plan details, but it provides no explicit guidance on when to choose it over the sibling tools or any exclusions. The resource is clear enough for basic selection, but the description leaves the decision entirely to inference.

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 burden of behavioral disclosure. It usefully reveals that only published articles are returned, results are newest-first, and filtering is optional. However, it does not describe output shape, pagination behavior, or any access constraints.

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 conveys the resource, scope, ordering, and optional filtering with no wasted words. The key behavioral details are 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 an unannotated tool with no output schema, the description covers the core purpose and selection criteria but omits guidance on choosing among sibling listing/search tools and does not describe the returned article fields. It is adequate for basic invocation but not fully self-sufficient.

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%, so the schema already documents all three parameters. The description adds only the notion that the category filter is applied to one ANSWER category, which is already implied by the schema. No meaningful extra parameter semantics are provided.

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 verb 'list', the resource 'published HiBot articles', the topic scope, the sort order, and the optional category filter. It does not explicitly contrast itself with siblings like search_blog or get_blog_article, so it falls short of full 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?

The description implies a browsing/listing use case and introduces the optional category filter, but it gives no explicit guidance on when to choose this tool over search_blog or get_blog_article. No exclusions or alternative routing are mentioned.

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, the description carries the full burden. It usefully discloses that the catalog is public and that each item includes a headline finding and page URL, implying a read-only listing operation. However, it does not mention pagination, ordering, or whether the list is complete or truncated, which are relevant for a no-parameter list endpoint.

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 sentence that front-loads the key fact ('public catalog'), gives concrete examples, and states the output format. Every phrase earns its place with no redundant 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 zero-parameter list tool with no output schema, the description sufficiently explains what the result contains. It could be slightly stronger by explicitly contrasting with get_case_study for full details, but the catalog framing makes the intended use reasonably clear.

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 input schema has zero parameters and 100% schema description coverage, so the baseline is 4. The description adds no parameter details, but none are needed since the tool takes no arguments.

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 ('list') and resource ('public catalog of real HiBot audit case studies'), provides example entries, and specifies the returned content (headline finding and page URL). This clearly distinguishes it from the sibling get_case_study, which implies retrieving a single detailed case study.

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 a catalog-browsing use case, which is reasonable for a zero-parameter list tool, but it does not explicitly say when to prefer this over get_case_study or list_blog_articles. The context is clear enough for a simple tool, but the guidance is implicit rather than explicit.

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 behavioral burden. It usefully discloses that results are relevance-ranked and include page URLs, but it does not state whether full article content, titles, or snippets are returned, nor any search limitations. This is adequate but not rich.

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 wasted words. 'Keyword search' appears first, and every remaining phrase ('published', 'ranked by relevance', 'page URL') adds useful distinguishing 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?

For a simple two-parameter search tool, the description covers the core behavior, result ranking, and a key return field. It is slightly thin on the full response shape due to the absence of an output schema, but it is sufficient for correct selection and 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 100%, so the schema already documents both parameters fully. The description adds no parameter-specific meaning beyond implying the query keyword maps to the query parameter, which is sufficient but not additive.

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 ('Keyword search') on a specific resource ('published HiBot articles') and adds distinctive details: relevance ranking and each result includes its page URL. This clearly differentiates it from siblings like list_blog_articles or get_blog_article.

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 'Keyword search' clearly establishes the intended use case: finding articles by search terms rather than listing all or fetching a specific article. It does not explicitly mention alternatives or exclusions, but the context is unambiguous 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.