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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool returns a 'compact cited answer' rather than raw retrieval hits, which provides additional behavioral context about the response format. However, it could elaborate more on the nature of citations or any side effects, though annotations already cover safety.

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 at two sentences, with no redundant or filler content. It front-loads the core purpose and immediately provides usage guidance, making every sentence earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple query tool with good annotations and clear purpose, the description is mostly complete. It covers purpose, usage guidance, and distinguishes from a key sibling. However, it lacks details about the return structure (e.g., format of cited answer) and does not mention the top_k parameter. Still, it is sufficient for an agent to understand and 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 2 parameters (query, top_k) with 0% description coverage in schema. The tool description only implicitly mentions the query via context, but does not explain top_k or specify formats. Given the low schema coverage, the description should compensate but fails to add meaningful parameter semantics beyond the parameter names.

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 asks Aruba/HPE docs for a compact cited answer. It distinguishes itself from siblings like lookup_api by specifying usage for prose/how-to questions versus exact endpoint/schema questions, making its purpose distinct and unambiguous.

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 provides usage context: use for prose/how-to questions when a short answer is desired, and not for exact endpoint/schema questions which should use lookup_api. This gives clear when-to-use and when-not-to-use 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 indicate readonly, idempotent, non-destructive. The description reinforces this with 'pure, bounded, read-only evaluation only' and details bounds, error handling, and lack of write operations. 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?

The description is lengthy but well-structured with clear sections. It front-loads purpose and provides detailed arg descriptions. Slightly verbose but every sentence adds value.

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 (4 params, output schema exists), the description covers all necessary context: return shape, bounds, error behavior, artifact handling. Complete without 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 description coverage is 0%, but the description compensates fully by explaining each parameter: observations (bounded list), policy (detailed structure with field/operator/expected), policy_id (free-text label), max_result_entries (default+max).

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 evaluates already-retrieved observations against a compliance policy. It distinguishes itself from data-fetching tools by explicitly noting it never fetches data itself.

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 says to fetch data first using invoke_read_tool, then pass it here. It gives guidance on what not to do (no backend calls). However, it does not compare with sibling tools like plan_reconciliation_schedule.

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 and destructiveHint=false, so the tool's safety profile is clear. The description adds that the search is by name, MAC, or IP, but no additional behavioral details (e.g., case sensitivity, partial matching). 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?

The description is a single 7-word sentence: 'Find a client by name / MAC / IP.' It is front-loaded, concise, and contains no extraneous information. Every word is necessary.

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 low complexity (1 required parameter, no enums, no nested objects) and the presence of an output schema, the description is complete. It clearly explains what the tool does and what the input parameter represents.

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 0% description coverage for parameters, but the description compensates by stating the query can be a name, MAC, or IP. This adds necessary context to the otherwise bare 'query' string parameter, helping the agent understand valid input types.

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 a client by name / MAC / IP.' It specifies the verb 'find,' the resource 'client,' and the search criteria, distinguishing it from siblings like find_device (searches devices) and find_tool (searches 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 implies usage when you have a name, MAC, or IP to identify a client, but it does not provide explicit guidance on when to use this tool versus alternatives, such as list_devices or lookup_api. No exclusions or alternate tool mentions are given.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no additional behavioral traits beyond stating the search criterion, which is consistent with 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 a single sentence that is front-loaded with the verb and resource. Every word earns its place; there is no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and full annotations, the description is complete. It tells the agent exactly what the tool does and how to use it. The presence of an output schema means return values are adequately documented elsewhere.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds meaning by stating 'by serial number', indicating that the 'query' parameter should contain a serial number. This compensates for the lack of parameter description in 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 action 'Find' and the resource 'device', with the specific criterion 'by serial number'. This distinguishes it from sibling tools like list_devices (which lists all devices) and find_client/find_tool (different resources).

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 searching a specific device by serial number, providing clear context. It doesn't explicitly state when not to use or mention alternatives, but the differentiation is clear given sibling tool names.

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 (readOnlyHint, idempotentHint, destructiveHint), the description reveals deduplication, match annotation types, safety flag mirroring, and compact default results. 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 well-structured with a clear opening, usage note, result details, and parameter list. Some redundancy could be trimmed, but overall effective and 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?

Given the tool's complexity (8 parameters, output schema), the description covers all essential aspects: usage, filters, defaults, and integration with sibling tools. Output schema exists so return values need not be explained.

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?

Despite 0% schema description coverage, the tool description provides detailed explanations for all 8 parameters in the Args section, adding significant 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?

The description clearly states the tool finds tools by query using semantic search and keyword matching. It distinguishes itself from sibling tools like 'search_docs' and 'invoke_tool' by being the discovery step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Call this first when you need an action.' Provides actionable guidance on how to use the returned name with invoke_read_tool or invoke_tool, and when to set include_schema=True.

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, and destructiveHint, so the description's role is lighter. It adds value by specifying the exact return value (global scope-id), which is not fully covered by 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?

Single sentence, front-loaded, no superfluous text. Every word is necessary and informative.

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 no parameters, a rich set of annotations, and an output schema, the description is complete for its simplicity. It clearly states the output.

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?

No parameters exist, so the description cannot add parameter semantics. With zero parameters, baseline score is 4 per guidelines.

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 'return' and the specific resource 'global (org-wide) scope-id', effectively distinguishing it from siblings like list_scopes which returns multiple scopes.

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?

No guidance on when to use this tool versus alternatives like list_scopes or other sibling tools. The description is purely declarative without usage 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, and destructiveHint. The description adds the detail that it refuses tools not annotated read-only and provides thorough cursor behavior explanation. 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?

Description is concise with a clear first sentence stating purpose, followed by a structured Args section. No unnecessary text, though cursor explanation could be slightly trimmed.

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 annotations, the description provides sufficient context for safe usage. Missing details about the 'arguments' parameter and output are minor; overall it is complete for an invocation 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 has 3 parameters with 0% coverage. Description explains 'name' as the tool name and 'cursor' in detail, but does not explain the 'arguments' parameter, which is generic and might require clarification. Partial compensation.

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 calls a read-only Aruba tool by name from find_tool, distinguishing it from invoke_tool which is for write/destructive tools. The verb 'call' and resource 'read-only Aruba tool' are specific.

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 to use this for read-only tools and to use invoke_tool for write/destructive tools only after explicit user intent. Provides clear when-to-use and when-not-to-use 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 indicate read-only and idempotent. The description adds detail: sequential dispatch (no concurrency), per-call rate limiting, failure isolation (never aborts rest), and budget handling. This adds 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 comprehensive but lengthy. It is well-structured with paragraphs and bullet points, but could be more concise. Some detail, like the budget explanation, could be shortened.

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 (batch, validation, rate limiting, budget), the description is thorough. It covers behavior, validation, response structure, and edge cases. Output schema exists, so return values are sufficiently documented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% at the top level, but the description explains each field within the batch call: name, arguments, id, cursor, with bounds (max 25 calls, argument size, nesting). It adds meaning beyond the schema's inline 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 it dispatches a bounded, ordered batch of read-only tool calls. It uses specific verbs like 'dispatch' and 'batch', and contrasts with the single-call sibling invoke_read_tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains when to use: for multiple read-only calls to reduce round trips. It notes sequential dispatch and failure handling. It doesn't explicitly list when not to use, but the read-only hint and mention of write rejection imply exclusion.

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?

Describes FastMCP dispatch, context injection for confirmation, and schema stripping. Adds detail beyond annotations (`destructiveHint: true`).

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?

Concise paragraph with example and explanation. Every sentence adds value, though slightly longer than necessary.

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?

No output schema and description does not explain return value (e.g., what the called tool returns). Omits error handling or side effects beyond destructive 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?

Schema coverage is 0%, so description compensates minimally by explaining `name` comes from `find_tool` and `arguments` is a kwargs dict with an example. No per-parameter details but sufficient for basic use.

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 it calls an Aruba tool by name with arguments dict, distinguishes from `invoke_read_tool` and references `find_tool` for name discovery.

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 example, explains that arguments get FastMCP validation, and notes it handles destructive ops requiring confirmation. Doesn't explicitly contrast with `invoke_read_tool` but context implies it.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds 'paginated' as behavioral context. No contradiction, but adds limited extra value 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?

Single sentence with no redundancy. Front-loaded with core purpose and pagination hint. 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?

Tool has output schema and is simple. Description is adequate for a basic list, but lacks differentiation from sibling find_device. Could mention it returns all devices versus filtering.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% with two parameters (limit, offset) lacking descriptions. The description hints at pagination, which implies their use. This compensates well, though explicit mapping would improve clarity.

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 explicitly states 'List devices (paginated).' with a clear verb and resource. Distinguishes from sibling tools like find_device (specific device search) and list_sites (different resource).

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?

No explicit guidance on when to use vs alternatives. Usage is implied for browsing all devices, but no exclusions or references to sibling tools like find_device for specific searches.

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 and destructiveHint, so the safety profile is clear. The description adds that the response is paginated and returns ID and name, but does not explain the behavior of the full_list parameter or other relevant traits.

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, concise sentence that is front-loaded with the key information. Every word adds value, and there is no unnecessary 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 tool is simple and has an output schema, so the description is partially adequate. However, it fails to explain the full_list parameter or usage context, leaving gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, yet the description does not explain the purpose or behavior of the three parameters (limit, offset, full_list). The agent cannot determine how to use these parameters from the description 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 verb 'List' and the resource 'Central scopes', specifying that they include sites, groups, and global. It also mentions the returned data (ID + name) and pagination. This distinguishes it from sibling tools like list_sites.

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 guidance on when to use this tool versus alternatives such as list_sites. It does not mention any prerequisites or context where one would prefer this over other listing tools.

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, openWorldHint, idempotentHint, destructiveHint. Description adds 'paginated', a useful behavioral detail, but omits other aspects like sorting or filtering.

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?

One sentence, no redundant words. Clearly front-loaded with 'List sites'.

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?

Output schema exists, so return values not needed. Tool is simple; description and annotations cover core behavior. Could add clarification on what constitutes a 'site' but not critical given sibling context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. 'Paginated' implies limit/offset control pagination, but doesn't explain defaults (50, 0) or semantics. Partial compensation.

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 clearly states verb 'list' and resource 'sites', and notes 'paginated' which distinguishes from potential non-paginated siblings. However, it could be more specific about the scope or criteria.

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?

No explicit guidance on when to use vs. alternatives like list_devices or list_scopes. Agent must infer from resource name 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, destructiveHint=false, and idempotentHint=true, indicating safe read operations. The description adds value by disclosing that the tool returns an empty array when it lacks a confident answer, which is useful behavioral context 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—four short sentences—front-loaded with the core purpose, followed by clear usage guidelines. Every sentence contributes essential information 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?

The tool has no output schema, so the description should clarify what the tool returns. It mentions 'endpoints, schemas, fields, enum values' but does not describe the response structure. Parameter semantics are missing, leaving the agent partially informed. Despite good usage guidance, the lack of param and output detail limits 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 0%, so the description must compensate by explaining parameters. However, the description does not mention 'query' or 'top_k' at all, providing no additional meaning beyond the schema's basic type and default. This is a significant gap.

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 an 'Exact Aruba Central API lookup — endpoints, schemas, fields, enum values.' It uses specific verbs and resources, and explicitly distinguishes itself from the sibling 'search_docs' tool by listing concrete use cases.

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 explicit when-to-use guidance: 'Use INSTEAD of search_docs for ...' with specific examples, and also provides fallback instructions: 'Returns [] when the specs hold no confident answer — fall back to search_docs in that case.' This fully clarifies when to use this tool versus alternatives.

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 (readOnlyHint, idempotentHint, etc.) are supplemented by the description detailing that the tool never writes to disk, always returns dry_run=True, and excludes write/destructive tools. 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?

The description is relatively long but well-structured, front-loading the core purpose. Each sentence adds value, though some details could be slightly condensed. Overall efficient given complexity.

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 input parameters, behavioral constraints, return values (ok, cadence, entries, excluded, dry_run, artifact), and mentions output schema indirectly. Complete for a complex read-only tool with good 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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: cadence with examples, tools, platforms, servers, max_entries (including default and max). Adds significant meaning beyond the basic schema types.

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: 'Build a bounded, read-only, plan-only recurring reconciliation schedule.' It distinguishes from sibling tools by emphasizing that it never creates a real schedule or executes a tool, providing a specific verb+resource and scope.

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: it only validates a cadence and produces a schedule specification. It explicitly says it never creates OS timers, cron jobs, or executes tools, guiding the agent to use siblings for actual execution. This provides clear context and exclusions.

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, openWorldHint, idempotentHint, destructiveHint=false. The description adds value by detailing that tool references are checked against the loaded catalog, never guessed, and unresolved references reported explicitly. 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 thorough but slightly long. However, it is well-structured with clear sections and bullet-like list for step spec fields. Could be 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?

Given the complexity of a planning tool with nested steps and dependencies, the description covers all aspects: input parameters, constraints (max 25 steps), resolution logic, return fields, and behavior. Output schema exists but description adds necessary context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully explains both parameters: steps (bounded max 25, each step dict with id, tool, hint, depends_on) and include_candidates (default false, returns up to 5 candidates). Also explains return fields in detail.

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 builds a deterministic, read-only dependency/order plan across enabled backend tools. It uses specific verbs and resources, and distinguishes itself from siblings like invoke_tool and find_tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Never executes any tool' and explains when to use (planning dependencies) and when not (execution). Provides alternative: find_tool for searching tools.

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?

Adds value beyond annotations by describing it as 'fuzzy retrieval' and noting lookup_api is 'lossless'. Annotations already declare readOnlyHint and idempotentHint, which are consistent with search behavior.

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

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

Two sentences front-load purpose and provide usage guidance concisely. No wasted 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?

No output schema and no parameter details. Does not explain return values or differentiate fully from siblings like ask_docs. Incomplete for a search tool with multiple parameters.

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?

With 0% schema description coverage, the description fails to explain the meaning of parameters like top_k and source. Only implies query usage via context. Minimal help for 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 it searches Aruba/HPE documentation with specific topics (Central config, APIs, NAC, VSG). It distinguishes from lookup_api, but could specify the exact scope of documentation.

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 lookup_api for exact API questions, offering a clear alternative. Does not address other siblings like ask_docs, limiting comprehensive guidance.

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