Sleepwalker

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

Description adds significant detail beyond annotations: explains effects on probes, credits, billing_pending, and idempotency. 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?

Description is longer but well-structured and informative. Each sentence adds value. Could be slightly more concise but still effective.

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?

Covers edge cases (stuck runs, multiple cancellations, final state), references status tool. With output schema present, return values are not needed. Complete for a mutation tool.

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% for the single parameter. Description doesn't add much beyond schema, but mentions run_id comes from create/list tools, which provides minimal extra context.

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 cancels a queued visibility run, using the 'killswitch' analogy. It distinguishes from siblings like create and status tools.

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?

Explicitly says when to use: to stop a direct-URL run that is queued or running. Provides scenarios for calling again. Lacks explicit when-not-to-use but context is clear.

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 annotations, the description discloses that work is queued and asynchronous, that it returns quickly with a run_id, and that billing fields appear when credits are reserved. It also signals mutation/non-idempotency risk with the duplicate-runs warning and user-confirmation requirement, consistent 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?

Five short sentences, with purpose front-loaded, then parameter modes, safety, async behavior, and billing consequence. Every sentence adds operational value; 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 an async create tool with two mutually exclusive modes, the description covers confirmation, return payload (run_id), polling behavior, duplicate prevention, and billing. Combined with rich schema descriptions and output schema, nothing an agent needs before invoking 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?

Schema description coverage is 100%, so the schema carries the parameter meaning. The description restates url/test_id mode selection and reinforces idempotency_key's duplicate-prevention purpose, but adds little beyond what the property descriptions already say.

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 'Queue a persisted Content Intelligence run,' naming the specific action, resource, and persistence behavior. It then distinguishes the two invocation modes (new url vs saved test_id), which is enough to tell it apart from sibling tools like the visibility-run creators.

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 gate, 'Use only after explicit user confirmation,' and tells the agent to poll get_sleepwalker_content_run_status until terminal rather than creating duplicate runs. It does not explicitly enumerate exclusions or alternatives such as score_sleepwalker_content for quick scoring, so it falls just short of full when-not 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?

The description discloses that the tool queues asynchronous work, returns quickly with a run_id, requires status polling, and includes credit fields when credits are reserved. This goes well beyond the annotations; it could earn a 5 but does not detail failure or terminal outcomes beyond the status-tool pointer.

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?

Six dense sentences cover the core action, mode choice, platform/model conventions, pricing caveat, result recording, and response fields without repeating the schema. The most important 'queue vs poll' 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?

For a 10-parameter run-creation tool with an output schema, the description covers usage intent, async lifecycle, model and pricing semantics, platform aliases, and duplicate-run avoidance. Nothing an agent needs to invoke it correctly 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?

Schema coverage is 100%, and the description still adds materially by explaining matrix-mode models vs per-probe model pinning, accepted platform labels, idempotency for retries, and per-model credit pricing. It turns the schema's individual field descriptions into operational 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 description opens with a specific verb ('Queue') and object ('a saved AI Visibility run'), and immediately distinguishes the two input modes ('prompts x platforms or explicit probes'). This clearly separates it from sibling tools like create_sleepwalker_content_run and suggest_sleepwalker_visibility_prompts.

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 says when to use it ('when the user wants reportable probe results, not just prompt ideas') and tells the agent to poll get_sleepwalker_visibility_run_status until terminal instead of creating duplicate runs. It also points to list_sleepwalker_visibility_models for exact pricing, giving concrete alternative actions.

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 indicate readOnlyHint=false, openWorldHint=true, and idempotentHint=false, but the description adds context by stating it 'runs extraction and serialization internally,' implying internal processing beyond a simple read. However, it does not disclose potential side effects, rate limits, or idempotency implications. Since annotations carry some burden, this is a modest but not comprehensive disclosure.

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, front-loaded with the core action ('Find relevant content trends for a URL') and immediately followed by a clarifying note on output scope. No wasted words or redundant detail.

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 an output schema available, the return structure is covered elsewhere. The description adequately conveys the tool's purpose and high-level output ('compact industry, trend, and citation data only'). It could mention how parameters like country and language influence results, but the schema already explains those. For a discovery tool, this is sufficiently complete.

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 each parameter (url, country, language, extraction_mode) is documented. The tool description adds no new parameter semantics beyond the schema. Baseline of 3 applies because the schema does the heavy lifting and the description does not enhance parameter understanding.

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 function: 'Find relevant content trends for a URL.' It specifies the resource (URL), the verb (find), and the output (trends, industry, citation data). This distinguishes it from sibling tools like create_sleepwalker_content_run (which creates runs) or get_sleepwalker_content_run_status (which checks 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 description provides no explicit guidance on when to use this tool versus alternatives. It does not mention conditions, prerequisites, or exclusions. Given the many sibling tools, the lack of context on when to choose this over others leaves the agent to 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=true, idempotentHint=true, destructiveHint=false, so the description does not need to repeat those. The description adds the context of 'across recent runs' and 'recurring', which is consistent. No behavioral traits beyond what annotations provide are disclosed, but that is acceptable since annotations cover the key aspects.

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 immediately states the verb and resource. It is front-loaded with the core action and context, with no unnecessary words. Every word earns its place, making it concise and effective.

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 the tool has an output schema (so return values are already documented), the description covers the essential function: finding recurring recommendations across recent runs for a known test. It does not explain the minimum frequency threshold, but that is in the parameter schema. The complexity is moderate, and the description is sufficient for an agent to understand what the tool does.

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 100% description coverage, meaning each parameter (runs, test_id, min_frequency) already has a description. The tool's description does not add additional meaning to the parameters beyond what the schema provides. Based on the guidelines, baseline for high schema coverage is 3, so this score is appropriate.

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 purpose: 'Find recurring Content Intelligence recommendations across recent runs for a known test.' It specifies the verb 'find', the resource 'recurring Content Intelligence recommendations', and the scope 'across recent runs for a known test'. This distinguishes it from siblings like get_sleepwalker_test_results which return all results, not just consistent ones.

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 looking for recurring recommendations across runs, but it does not explicitly state when to use this tool versus alternatives. There is no mention of exclusions or specific scenarios. Given the sibling list includes many similar get/list tools, more guidance would be helpful, but the purpose itself provides some implicit context.

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 indicate read-only and idempotent behavior. The description adds value by explaining the polling pattern and when to include the result payload, aligning with annotations without contradiction.

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 concise with two front-loaded sentences. Each sentence provides essential information without unnecessary 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?

Given the tool's low complexity, good schema, annotations, and output schema, the description is largely complete. It covers main purpose, polling usage, and result inclusion. Minor improvements could include explicit differentiation from sibling tools for result retrieval.

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%, so the schema already documents both parameters well. The description rephrases the guidance for include_result but does not add significant new meaning beyond what the schema descriptions provide.

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 it reads status and optional full result for a persisted direct-URL Content Intelligence run. The verb 'Read' and resource 'status and optional full result' are specific, and the mention of 'Content Intelligence run' helps distinguish from similar sibling tools like get_sleepwalker_visibility_run_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 description provides implicit guidance by indicating to poll while queued or running and to set include_result only after completion. However, it does not explicitly compare to alternatives like get_sleepwalker_run_result or state when not to use 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral context beyond annotations: fetching is 'capped', supports pagination via offset and max_chars, and includes citation control. No contradictions 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?

Two sentences, front-loaded with purpose and key parameters, followed by prioritization guidance. Every sentence adds value; no redundancy or irrelevant details.

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 7 parameters and indexing complexity, the description covers main behaviors (capped, pagination, citation toggle, multiple selection methods). Output schema exists so return values need not be explained. Minor gaps like out-of-range handling, but overall 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 coverage is 100%, baseline 3. Description adds meaning by explaining how parameters relate: result_index vs platform+prompt_index, and the flattened list order matching status ca_result. This is valuable guidance beyond the schema field 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 clearly states the verb 'Fetch' and the resource 'one capped AI Visibility prompt response'. It distinguishes from siblings by specifying it is preferred over 'full include_results' for large runs, which is a distinct use case among sibling tools like get_sleepwalker_run_result.

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 provides clear guidance on parameter usage: use result_index or prompt_index for the flattened list, or platform+prompt_index for Nth prompt. It also recommends this tool over include_results for large runs. However, it does not explicitly state when not to use it, but the context is sufficient.

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 indicate read-only, idempotent, non-destructive. Description adds that it searches owned tests and fetches latest UI-visible runs, which is valuable behavioral context beyond annotations. No contradiction.

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?

Single sentence, front-loaded, no wasted words. Efficiently communicates core function.

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?

Covers essential purpose. With full schema and output schema (signal shows output schema exists), description is adequate. Minor omission: behavior when no results not mentioned, but not critical.

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 has 100% coverage; description adds context about 'owned' tests and 'UI-visible' runs, supplementing parameter descriptions. Adds 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?

Clearly states 'Find owned CI/AC tests by URL and fetch their latest UI-visible runs.' Provides specific verb (Find) and resource (owned CI/AC tests) and action (fetch runs). Distinguishes from sibling tools that create runs, get status, etc.

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?

Implies usage: when you have a URL and need to find tests and latest runs. But no explicit guidance on when to use vs alternatives or when not to use. Context is provided but not extensive.

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, idempotent, and non-destructive behavior. The description adds context about what kind of run is fetched and how direct-URL runs are handled, enhancing understanding beyond 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 three sentences, concise and well-structured. The most important information is first, and every sentence adds value without 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?

Given the simple tool (one required parameter, clear annotations, output schema present), the description fully covers the tool's purpose, usage boundaries, and parameter context. No gaps.

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 has full parameter coverage and describes the run_id parameter adequately. The description does not add significant new meaning beyond the schema, so a baseline score of 3 is appropriate.

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 ('Fetch') and the resource ('one full structured CI/AC run belonging to a saved test'). It distinguishes from sibling tools by mentioning that direct-URL runs and AC raw responses are handled elsewhere, providing specificity.

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 this tool (for saved test runs) and when not (for direct-URL runs or AC raw responses), naming alternative tools for those cases. This provides clear 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 declare the tool as read-only, idempotent, and non-destructive, so the description is not required to reiterate those. It adds useful context about the summary nature and date range, but does not disclose pagination, maximum results, or empty result behavior. This is acceptable given the annotations' completeness.

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 that conveys the core purpose efficiently without extraneous words. Every word 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?

Given the 5 optional parameters, rich schema, and output schema (present but not shown), the description suffices for an initial scan tool. It could mention that it returns summaries (not full data) and the abbreviation 'CI/AC' might be clarified, but overall it is sufficiently complete for agent understanding.

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 5 parameters have exhaustive descriptions in the schema (100% coverage), so the description adds minimal value beyond restating the date lookback. Baseline of 3 is appropriate as the schema carries the semantic load.

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 retrieves compact CI/AC run summaries for a date range, positioning it as a lightweight overview tool distinct from sibling tools that provide full run details or specific analyses. The verb 'scan' and the phrase 'before drilling into full runs' effectively signal its role.

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 implicitly advises using this tool for initial exploration before diving into more detailed tools, but it does not explicitly exclude scenarios or name specific alternatives. The guidance is clear enough for most agents but lacks explicit boundary conditions.

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 signal read-only and idempotent behavior. The description adds that the tool returns 'compact latest run history', which provides temporal context and format hint. No contradictions.

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, no redundancy, front-loaded purpose, and efficient reference to sibling tool.

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 only 2 parameters, rich annotations, and an output schema present, the description fully covers necessary context: what the tool does, when to use it, and output format hint.

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?

Input schema has 100% description coverage for both parameters (test_id and limit). The tool description adds no extra parameter-specific context beyond what the schema provides, so baseline score of 3 is appropriate.

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 'Fetch' and the resource 'compact latest run history for a known test'. It distinguishes from the sibling tool get_sleepwalker_run_result, which provides full details.

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?

Explicitly advises to use get_sleepwalker_run_result for full details, providing clear when-to-use and alternative 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 confirm readOnly, idempotent, non-destructive. Description adds polling behavior, result paging with ca_result reports results_total, and that summary covers full run. No contradictions.

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 sentences, front-loaded with main purpose, then usage guidance. No redundant or vague statements. Every sentence adds essential 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 complexity (5 params, 1 required) and presence of output schema, description covers polling, paging, include_results usage, and alternative tool. No missing context needed for agent decision-making.

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?

100% schema coverage, but description adds value by explaining when to use include_results (only after completion), paging with results_offset/results_limit, and referencing ca_result. Also mentions alternative tool for single answers.

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?

Clear verb 'Read' with specific resources 'status, probe progress, and optional results' for an AI Visibility run. Distinguishes from sibling tools like get_sleepwalker_content_run_status and get_sleepwalker_prompt_response by focusing on run-level 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?

Provides explicit guidance: poll compact status while queued/running, set include_results only after completion or when full data needed, page large runs with offset/limit, and mentions alternative get_sleepwalker_prompt_response for individual answers.

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 mark the tool as read-only and idempotent. The description adds value by detailing pagination behavior (has_more and next_starting_after), which is beyond what annotations provide. No contradictions.

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: first states the purpose, second provides pagination guidance. No unnecessary information or repetition. Efficiently structured.

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 full annotation coverage, complete schema descriptions, and an output schema, the description adds the missing piece of pagination behavior. Everything needed for correct invocation is present.

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% with parameter descriptions. The description further clarifies the relationship between the starting_after parameter and the response field next_starting_after, adding practical usage context 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 clearly states the verb 'list' and the resource 'persisted direct-URL Content Intelligence runs', specifying the domain ('Sleepwalker infrastructure actions'). It effectively distinguishes from sibling tools like list_sleepwalker_visibility_runs by naming the specific run type.

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 provides explicit pagination guidance ('When has_more is true, pass next_starting_after as starting_after'). While it does not mention when not to use the tool or alternatives, the pagination instruction is a clear usage directive.

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 as safe and idempotent. The description adds context about user-specific scoping ('authenticated user's'), which is valuable beyond annotations. No contradictions.

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, efficient sentence of 12 words, with no redundancy. Perfectly concise.

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 is simple with 3 optional params and an output schema. The description covers the core purpose and scope, though it could mention return type or pagination but these are covered by 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 100%, so the baseline is 3. The description adds minimal parameter insight beyond the schema, only implicitly linking to test_type values.

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 lists only 'Content Intelligence and AI Citations' tests for the authenticated user, with a specific verb and resource. The title and name align, and it distinguishes from sibling tools that list runs.

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 listing tests (as opposed to runs from siblings), but no explicit when-to-use or when-not-to-use guidance is provided. It is clear in context.

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, idempotentHint, destructiveHint. The description adds value by stating it is 'free' and describing the return structure in detail, which goes beyond the basic safety 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 two sentences plus a one-sentence footer, all relevant and front-loaded. Every sentence adds value without 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?

Given no parameters and the presence of an output schema, the description fully explains the output structure and usage. It is complete for 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?

There are zero parameters, so the baseline is 4. The description adds context about the output schema, which is sufficient since no parameter documentation is needed.

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 it lists AI models for visibility runs, per platform, with prices. It specifies the exact resource and verb, and distinguishes from sibling tools like list_sleepwalker_content_runs by focusing on visibility models.

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 explains how to use the output ('Use the ids or role keywords...when creating a run'). It also notes it is read-only and free, implying safe usage. However, it does not explicitly contrast with alternative tools when to use this vs others.

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 indicate readOnly and idempotent. Description adds context that it lists 'queued' runs, but does not detail any side effects beyond what annotations cover.

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 succinct sentences: first states purpose, second provides critical pagination usage. No redundant 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?

With 3 well-described parameters, an output schema, and clear pagination guidance, the description is fully adequate for an agent to use the tool correctly.

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 has 100% description coverage for all parameters, so the tool description does not need to add much. It mentions pagination but does not elaborate on parameter meanings beyond 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?

Description specifies 'List queued infrastructure AI Visibility runs for the authenticated user', which is a clear verb+resource pair and differentiates from sibling tools like list_sleepwalker_content_runs.

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?

Provides explicit pagination guidance ('When has_more is true, pass next_starting_after as starting_after'), but does not contrast with alternative tools for similar tasks.

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?

The description discloses a key behavioral fact not captured by annotations: the scoring does not save a run. It also reveals the performance consequence of omitting trends. Annotations already cover destructive and read-only hints, and nothing in the description contradicts them, so the added behavioral context is meaningful without being excessive.

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, no filler. The primary action and non-persistence caveat are front-loaded, and the trends guidance is delivered in a compact second sentence that 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?

With an output schema present and all parameters documented in the schema, the description is largely complete for making a correct call. The only minor gap is that the description doesn't explicitly route the caller to a sibling tool when saving a run is desired, though the 'without saving a run' phrasing makes the distinction inferable.

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%, so the baseline is 3. The description adds value beyond the schema by advising callers to pass compact trends from discover_sleepwalker_content_trends and explaining the slower behavior when trends are omitted. This is genuinely useful parameter-level guidance even though the schema documents all fields.

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: 'Score a URL with Content Intelligence without saving a run.' The phrase 'without saving a run' clearly differentiates this from create_sleepwalker_content_run and makes the tool's purpose immediately distinguishable from sibling tools.

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 operational guidance: prefer passing compact trends from discover_sleepwalker_content_trends, and warns that omitting trends causes slower discovery. It implies the no-save use case but does not explicitly name create_sleepwalker_content_run as the alternative when persistence is needed, so it falls just short of full exclusion 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 only provide generic hints; readOnlyHint is false, so the description's statement that it does not save a run adds useful behavioral clarity. However, it does not disclose more about side effects, fetching behavior, or what 'normalized' means in practice, leaving some ambiguity for an operation that is not explicitly read-only.

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, front-loaded with the primary use case and followed by crisp exclusions. There is no redundant or filler text.

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 description, combined with a fully documented input schema and an output schema, covers the key selection and invocation context. It could add detail about pagination or output shape, but the exclusions and normalized-view framing make it sufficient for an agent to decide whether 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 input schema has 100% parameter description coverage, so the schema already documents url, offset, max_chars, and extraction_mode. The description adds little about how parameters behave or relate to the normalized content view, so it merits the baseline score rather than higher.

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 specific purpose: providing Sleepwalker's normalized page content view. It also distinguishes this tool from siblings by listing what it does not do (score content, discover trends, suggest prompts, save a run).

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 an explicit 'use this only when' condition and clearly states exclusions, but it does not name the specific sibling tools that should be used instead. The exclusions imply alternatives like score_sleepwalker_content or discover_sleepwalker_content_trends without directly referencing them.

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?

The description states 'does not query AI platforms or save a visibility run,' which implies no side effects and read-only behavior. However, the annotations declare readOnlyHint: false, which contradicts the description's implied read-only nature. This is a clear annotation contradiction, so the score is 1.

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 states the primary function, and the second clarifies scope and exclusions. There is zero redundancy, and the most important scoping information is front-loaded, making it easy for an agent to quickly assess applicability.

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 description covers the tool's purpose and explicitly notes it has no side effects, which is essential context. However, the contradiction with readOnlyHint creates ambiguity, and the description does not hint at what the output (prompt suggestions) looks like or how many are returned, though the output schema exists. Given the contradiction and minor gaps, completeness is adequate but not excellent.

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 100% coverage—every parameter (url, brand_name, country, language) has a description with format details (ISO codes, defaults). The tool description adds no additional parameter-specific meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 ('Suggest') and resource ('AI Visibility prompts for a URL'), making the tool's purpose immediately clear. It also differentiates from siblings by explicitly noting it does not query AI platforms or save a visibility run, distinguishing it from run-creation and execution tools.

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 usage context: it is for prompt ideas only, not for actual queries or runs. It does not explicitly name an alternative tool, but the exclusion of 'query AI platforms' and 'save a visibility run' implies that for those operations one should use other sleepwalker tools. This is sufficient guidance but lacks an explicit 'use X instead' statement.

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