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?

Annotations provide basic hints (not readOnly, not idempotent, not destructive). The description adds valuable context: it queues work, returns quickly with run_id, instructs to poll for completion, and mentions billing fields on response. This goes beyond annotation details 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 five sentences, each contributing essential information. It front-loads the main action, then provides usage notes. 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?

Given the output schema exists (not shown), the description need not explain return values. It covers core behavior, usage constraints, polling advice, and billing context. It lacks error handling or prerequisites but is largely complete for a tool with 6 parameters and rich 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?

Schema coverage is 100%, so baseline is 3. The description repeats the url/test_id choice and mentions idempotency_key's purpose, but does not add new meaning for country, language, or analysis_depth beyond the schema descriptions. Marginal added value over 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 tool queues a persisted Content Intelligence run, specifying input options (url or test_id). It distinguishes the action from sibling tools like create_sleepwalker_visibility_run but does not explicitly differentiate beyond the core function.

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 advises using only after explicit user confirmation and recommends polling status to avoid duplicates. However, it does not specify when not to use this tool or mention alternatives, leaving usage context implied 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?

Annotations show readOnlyHint=false (write operation), destructiveHint=false, idempotentHint=false. The description adds context: it starts queued work, returns quickly with run_id, credits reserved, and polling needed. No contradictions; it enriches the behavioral model beyond what annotations provide.

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 well-structured with front-loaded purpose, then details. It is slightly long but every sentence contributes (models, platforms, credits, polling). Could be slightly more concise but still efficient.

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 complexity (10 params, modes, pricing, polling), the description is comprehensive. It covers usage modes, parameter dependencies, credit behavior, and links to related tools. Output schema exists, so return value details are not needed.

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 existing descriptions, but the tool description adds significant meaning: explains the two modes (matrix vs explicit probes), model keywords (latest/prior/default), platform slugs, pricing implications (per-probe credits, defaults), and idempotency. It clarifies usage patterns not evident from schema alone.

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 queues a saved AI Visibility run from prompts x platforms or explicit probes, distinguishes it from getting just prompt ideas (reportable results vs ideas), and specifies the verb 'queue' and resource 'AI visibility 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 advises using this tool when the user wants reportable probe results, not prompt ideas, and recommends polling with get_sleepwalker_visibility_run_status to avoid duplicates. It could be more explicit about when not to use it (e.g., only for ideas use suggest_sleepwalker_visibility_prompts), but the guidance 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?

Annotations already declare non-readOnly and openWorldHint. The description adds that it runs extraction/serialization and returns data only, but does not clarify side effects (e.g., whether it creates runs or persists data). Minimal extra behavioral context 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 concise with two sentences: first states the core action, second explains internal process and output. No unnecessary words, and it is front-loaded with the main purpose.

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 existence of an output schema and comprehensive annotations, the description is largely complete. However, it lacks guidance on when to use this tool versus similar siblings, which would improve completeness.

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% with detailed parameter descriptions (e.g., URL, country, language, extraction mode). The tool description adds no additional parameter meaning, 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 tool's purpose: 'Find relevant content trends for a URL'. It also specifies that it runs extraction and serialization internally and returns compact industry, trend, and citation data, which distinguishes it from siblings like serialize_sleepwalker_page_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?

The description implies usage by mentioning internal integration, but it does not explicitly state when to use this tool over alternatives like create_sleepwalker_content_run or serialize_sleepwalker_page_content. No when-not or exclusion criteria are provided.

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?

Annotations indicate readOnlyHint=false and destructiveHint=false, so the tool is neither read-only nor destructive. The description adds behavioral context: it does not save a run (so no persistent state), and omitting trends slows performance. No contradiction with annotations; description provides useful behavior 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 two sentences long, with the first sentence stating the purpose and the second providing usage guidance. No unnecessary words; every sentence 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 6 parameters (1 required), 100% schema coverage, and an output schema (not shown but indicated as present), the description covers the core action, optimal usage with trends, and distinguishes from sibling tools. No gaps remain; it is complete for the tool's complexity.

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 baseline is 3. The description adds value by advising to pass 'compact trends from discover_sleepwalker_content_trends', which clarifies the source and optimization for the trends parameter. This goes beyond the schema's description and helps agents understand parameter usage.

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: 'Score a URL with Content Intelligence without saving a run.' It uses a specific verb ('score') and resource ('URL with Content Intelligence'), and distinguishes itself from saving a run, which differentiates it from siblings like create_sleepwalker_content_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 provides explicit usage guidance: 'Prefer passing compact trends from discover_sleepwalker_content_trends; omitting trends makes Sleepwalker discover them first and can be slower.' It tells when to use trends and notes the alternative (omitting trends leads to slower discovery), offering clear context for when to use this tool 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?

The description adds transparency by stating the tool does not save a run, but annotations are incomplete (readOnlyHint=false, openWorldHint=true). It does not disclose authentication needs, rate limits, or whether it modifies any state beyond not saving a run, leaving behavioral gaps.

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 highly concise: one sentence with a clear core purpose and a negative list to differentiate. It is front-loaded and contains no superfluous words.

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 presence of an output schema and full parameter schema coverage, the description is fairly complete. It explains the tool's non-behaviors but could briefly state what the output contains (e.g., 'returns structured text content'). Still, it adequately covers the essential context for selection.

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 parameters. The tool description adds no additional meaning to the parameters beyond what the schema provides, meeting the baseline of 3.

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 provides 'Sleepwalker's normalized page content view', which aligns with the name serialize. However, it could more explicitly state that it extracts and returns raw page content from a URL, making the purpose slightly ambiguous.

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 instructs 'Use this only when you need Sleepwalker's normalized page content view' and lists what it does not do (score, discover, suggest, save), effectively guiding the agent to alternatives like score_sleepwalker_content or discover_sleepwalker_content_trends. It lacks explicit when-not-to-use scenarios.

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 provide readOnlyHint=false and other hints, but the description adds critical context: it does not query or save, clarifying it is effectively read-only despite the hint. This is valuable beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two sentences, no extraneous information, and the purpose is front-loaded in the first sentence.

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 simplicity and the presence of an output schema, the description is complete: it states what it does, what it does not do, and implies its non-destructive nature. No gaps remain.

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 is already explained. The tool description does not add further parameter details beyond the schema, so baseline score of 3 applies.

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 suggests AI visibility prompts for a URL, with a specific verb ('suggest') and resource ('prompts'). It explicitly distinguishes itself from other tools by noting it does not query or save, which differentiates it from siblings like 'create_sleepwalker_visibility_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 includes explicit usage guidance: 'Use this for prompt ideas only; it does not query AI platforms or save a visibility run.' This tells the agent when to use it and what it does not do, though it does not name alternative tools directly.

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