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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses no auth requirements, rate limits, pagination behavior, or how the default limit of 5 is applied. It conveys what is searched but nothing about how the operation behaves.

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

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

Two sentences, front-loaded with the core action followed by the valid enum values, with zero filler. Every clause 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?

Although an output schema exists (so return values need no explanation), the absence of annotations and 0% schema coverage means the description should carry behavioral and parameter detail it omits. It never explains the limit parameter or the default result set, leaving real gaps for invocation.

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

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

Schema description coverage is 0%, so the description is the only source: it lists the valid values for the type and severity parameters, which substitutes for missing schema enums. However, the limit parameter (default 5) is never mentioned, leaving one of three parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search) and resource (classified AI agent security incidents), and the enumerated incident types and severities make the domain unambiguous. An agent can readily distinguish this from siblings like get_incident_stats or get_ai_regulations.

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 never says when to use this tool versus the alternatives. It does not mention get_incident_stats (statistics) as a sibling for aggregate views, nor does it state prerequisites or the conditions that select this tool.

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

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

No annotations are provided, so the description must carry the full behavioral burden. It does not disclose pagination behavior (despite a 'limit' parameter), result freshness, data source, or any rate limits. For a search tool with zero annotation coverage, these gaps are significant.

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 with no waste, though it is so minimal that conciseness comes at the cost of content. It is appropriately 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?

An output schema exists, so the description needn't explain return values. However, for a search tool with three undocumented parameters (including a non-obvious 'remote' default) and no annotations, the description is incomplete—it should at minimum clarify parameter semantics and usage.

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%. The description provides no meaning for 'limit', 'remote' (which has a non-obvious default of -1), or 'company'. Because the schema itself is bare, the description should compensate but does not, leaving key semantics undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Search') and resource ('AI industry job postings'), which is more specific than most siblings in this set. However, it does not differentiate itself from siblings like get_company_intelligence, which could also surface hiring data, so it falls short of a 5.

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 says nothing about when to use this tool versus alternatives such as get_company_intelligence, nor does it state prerequisites like API keys or rate limits. No usage context is provided beyond the verb.

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

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

With no annotations, the description carries the full behavioral burden. It does not disclose whether pricing is live or cached, authentication requirements, rate limits, or any side effects. The word 'current' implies freshness but gives no concrete behavioral detail. The output schema exists, so return format need not be explained.

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

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

The description is two short sentences with zero waste and is front-loaded with the core purpose. The provider list is compact and useful.

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 (two optional parameters) and has an output schema, so return values need not be described. However, the description does not explain how the optional model and provider parameters behave when omitted, leaving a meaningful gap for an agent trying to call it 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 description coverage is 0%, and both parameters (model, provider) are undocumented. The provider list partially maps to the provider parameter, but the model parameter is entirely unexplained, including whether it accepts specific model names, is case-sensitive, or what happens when omitted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Get current pricing for AI models.' The provider list further narrows the scope to model pricing. However, it does not explicitly distinguish itself from the sibling tool get_gpu_pricing, so sibling differentiation is left to the agent.

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?

There is no guidance on when to use this tool versus alternatives such as get_gpu_pricing. The description only lists supported providers and offers no conditions, prerequisites, or 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?

With no annotations, the description carries the full behavioral burden and discloses almost nothing: not whether the call is read-only, what an empty search returns, how results are ordered, or how `limit` interacts with matching. It relies entirely on the reader inferring safe read behavior from the name.

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

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

A single short sentence is front-loaded and wastes no words, which is structurally good. However, the brevity here is under-specification rather than effective conciseness, so it does not earn a 4.

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?

An output schema exists, so return values need not be explained, but with zero annotation coverage, zero parameter documentation, and no usage framing, the definition leaves the agent guessing on nearly every invocation detail for a two-parameter 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 description coverage is 0%, so neither `limit` nor `search` is explained in the schema. The description adds no meaning either — it only loosely implies that a search term exists, without stating matching semantics, default behavior for an empty string, or what `limit` bounds.

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 pairs a specific verb ("Search") with a specific resource ("AI regulations and policy documents"), so an agent knows exactly what it retrieves. No sibling tool covers regulations or policy, so the lack of explicit sibling differentiation is not a real gap here.

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?

There is no statement of when to use this tool versus alternatives, no mention of prerequisites, and no condition under which it should be avoided. The only usage signal is the implied one carried by the word "Search," which is not enough for a 3.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose which signals drive the recommendation (live status, pricing, drift), but says nothing about whether this is a read-only computation, whether it queries external systems, latency, or any side effects.

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 short sentences, front-loaded with the action and followed by the accepted priority values. No filler, though the priority list could have been tied more explicitly to selection criteria.

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?

An output schema exists, so return values need not be described, and the priority values are covered. However, with no annotations and a read-style recommendation tool, the definition leaves the agent unsure about side effects and how priority actually changes the result.

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% and the single 'priority' parameter has no enum in the schema, so the description's enumeration of valid values (cheap, fast, reliable, balanced) and the default of 'balanced' is a genuine value-add beyond the structured field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — 'Get AI model routing recommendation' — and identifies the inputs it weighs (live status, pricing, drift), which distinguishes it from siblings like get_ai_status, get_ai_model_pricing and get_model_drift that return raw data. It stops short of describing the recommendation's form, so it is clear but not maximally differentiated.

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?

Naming the four priorities ('cheap, fast, reliable, balanced') implies when each choice applies, but there is no explicit statement of when to call this tool versus the sibling status/pricing/drift tools. Usage is implied rather than spelled out.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool reports online/offline status and latency, which is useful context beyond the name, but it omits details like data freshness, rate limits, or authentication requirements.

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

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

A single, front-loaded sentence with no redundant or filler content. Every word earns its place and the key information is presented immediately.

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, zero-parameter read tool with an output schema, the description is largely complete—it states the core function and the kind of information returned. The main missing piece is usage guidance, but the output schema covers return values, and the low complexity means little else is required.

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

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

The tool takes zero parameters, so there are no parameters for the description to explain. Per the rubric, this yields a baseline of 4; the description does not need to compensate for any schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Check') and resource ('AI providers online/offline with latency'), making the tool's function clear. It does not explicitly differentiate from siblings like get_ai_incidents or get_ai_route, but the scope is distinct enough for an agent to identify.

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 no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The implied use case (checking provider status) is inferable but not stated.

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

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

With no annotations, the description carries the full burden, and it only implies that the response bundles incidents, jobs, and funding data. It says nothing about permissions, caching/staleness, coverage limits, or what an empty entity argument does, so the behavioral profile is largely undisclosed.

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

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

A single short sentence with no filler, and the aggregated signal list is front-loaded. It is efficiently structured, though the fragment style leans on the reader to fill in the verb.

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?

An output schema exists, so return values need not be explained, but the description still leaves the one input parameter undefined and offers no usage context. For a simple single-argument aggregation tool that is marginally adequate rather than complete.

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

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

Schema description coverage is 0% and the single 'entity' parameter is undocumented. The phrase 'per AI company' hints that entity is a company name or identifier, but the description does not confirm the expected format, and the default of "" (which presumably means all companies) is never explained.

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 phrase 'Cross-signal intelligence: incidents + jobs + funding per AI company' names the resource (AI company) and the aggregated signals, which distinguishes it from single-signal siblings like get_ai_incidents and get_ai_jobs. It is a noun phrase rather than a verb, but the scope is specific enough for an agent to recognize it as a consolidated per-company view.

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?

There is no explicit when-to-use guidance, no prerequisites, and no named alternatives. The 'cross-signal' framing weakly implies it should be preferred over the individual incident/job tools for a consolidated view, but that inference is left entirely to the agent.

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

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

No annotations exist, so the description carries the full burden, yet it says nothing about data freshness, caching, rate limits, or auth requirements beyond 'current'. For a pricing lookup that agents may rely on for cost decisions, the liveness/source behavior should be disclosed.

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 short sentences, front-loaded with the purpose and then the supported providers. No wasted words, though it is terse to the point of omitting useful detail.

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

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

An output schema exists, so return shape need not be explained. However, with zero annotations and zero schema description coverage, the description leaves parameter semantics and behavioral traits (freshness, filtering) unaddressed, so it is only minimally adequate.

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% for both parameters. The description partially compensates for 'provider' by naming valid providers, but gives no format or expected values for 'gpu', leaving half the parameters undocumented anywhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb+resource ('Get current GPU cloud pricing') and enumerates the covered providers, which cleanly separates it from the sibling get_ai_model_pricing. It does not explicitly reference siblings, keeping it at 4 rather than 5.

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 when-to-use or when-not-to-use guidance is given. The provider list implies the tool's scope but never tells the agent when to prefer this over get_ai_model_pricing or how to frame a query.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The verb 'get' weakly implies a read-only operation, but the description does not state read-only safety, authorization needs, side effects, or other behavioral 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, front-loaded sentence with no wasted words. It states the tool's output dimensions directly and efficiently.

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, zero parameters, and the presence of an output schema, the description is nearly complete. It explains the aggregation dimensions (type and severity) without needing to describe return values, though it omits usage context relative to sibling incident tools.

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

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

The tool takes zero parameters, so there are no parameter semantics to document. The baseline for zero-parameter tools is 4, and the description does not need to compensate for any schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'get total AI security incident counts by type and severity.' This clearly distinguishes the tool from siblings that list incidents, such as get_ai_incidents, by signaling an aggregate/count view rather than individual records. However, it does not explicitly name or differentiate against the closest sibling.

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?

There is no guidance on when to use this tool versus alternatives like get_ai_incidents. The aggregate nature of 'total counts' implies a reporting use case, but no explicit when/when-not context or prerequisites 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?

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about read-only safety, what 'drift observations' contain, whether results are paginated or capped, or how the defaults behave. Only the weak implication of 'Check' suggests a read operation.

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

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

It is a single short sentence with no wasted words and the purpose is front-loaded. However, extreme brevity here reflects under-specification rather than economy, so it cannot score higher.

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?

Although an output schema exists (so return values need not be explained), the tool has two wholly undocumented parameters and zero annotations, and the description never supplies the missing scope, filtering, or safety context. It is not complete enough for reliable invocation.

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

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

Schema description coverage is 0% for both parameters. The description never mentions 'days' (time window, default 7) or 'model' (default empty string, presumably meaning all models) — the empty-string default is particularly non-obvious and undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a resource ('AI model behavioral drift observations') but the verb 'Check' is vague and could mean fetch, validate, or alert on. It gives no differentiation from siblings like get_ai_status or get_ai_incidents, leaving the agent to guess which is the right drift-related 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?

There is no statement of when to use this tool versus alternatives, nor any mention of prerequisites, time windows, or filtering intent. The agent must infer everything from the 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?

No annotations are provided, so the description must carry the full behavioral burden. It does not disclose whether the scan is intrusive, requires authentication, has rate limits, or causes side effects—only the scan's purpose is stated.

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 direct sentence with no filler or redundancy, and the core purpose is front-loaded.

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

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

For a vulnerability scanning tool with no annotations and an undocumented input parameter, the description is too sparse. It omits target format, required permissions, and whether the scan is safe or non-intrusive; the existence of an output schema does not compensate for missing input 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 description coverage is 0% and the single required 'target' parameter has no schema description. The description implies 'target' refers to the n8n instance but does not specify its format (URL, hostname, IP) or any constraints, leaving the parameter largely undocumented.

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 has a specific verb ('Scan'), resource ('self-hosted n8n instance'), and target vulnerability ('CVE-2026-21858'), which clearly differentiates it from the read-only get_* sibling tools.

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

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

No guidance is given on when to use this tool versus alternatives, nor any prerequisites such as required credentials or network access. It only states what the tool does.

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