Agent Enhancer Utilities

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no further behavioral detail such as whether the utility is invoked or whether any state changes occur, but it also does not contradict 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?

A single sentence that front-loads the action and resource, then lists the relevant returned fields compactly. There is no filler or repetition of schema or annotation contents.

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 one required parameter, explicit return-field list, and read-only annotations, the description gives an agent enough to understand what the tool does and what it will receive. It omits minor guidance on where slugs come from or how this differs from list_tools/search_tools, but those are not essential for calling 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%, but the phrase 'for one utility' makes it clear that the required 'slug' parameter identifies which utility to describe. It does not explicitly say 'slug is the unique identifier' or provide an example, but for a single simple string parameter the intended meaning is reasonably inferable.

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

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

The description names a specific action ('Get'), a clear resource ('one utility'), and enumerates the exact information returned: schema, price, limits, side effects, retention, and HTTP endpoint. The 'for one utility' scope also distinguishes it from sibling tools like list_tools and search_tools, which enumerate or locate utilities.

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 use when an agent needs detailed metadata for a single known utility, but it does not explicitly say when to use this tool instead of search_tools or list_tools. No alternative tools or exclusion conditions are mentioned, so usage guidance is only implicit.

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 idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context by signaling that this is a polling operation and that the status_token is private/secret, which affects how the agent should handle and treat the parameter. 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?

The description is a single, well-structured sentence that front-loads the action ('Poll') and includes both key qualifiers ('previously submitted', 'private status token'). There is no redundancy or filler.

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

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

For a simple two-parameter polling tool with readOnly/idempotent annotations, the description is largely sufficient: it names both required inputs and clarifies the operation. It does not describe the response shape or possible status values, but the tool has no output schema and the title/description already communicate 'status', so this is a minor gap rather than a critical omission.

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 carries the burden of explaining parameters. It does provide some semantic value: request_id maps to a 'previously submitted capability request' and status_token is characterized as 'private'. However, it does not explain how to obtain these values or give format/format expectations beyond the schema's length constraints.

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 uses a specific verb ('Poll') with a clear resource ('capability request') and outcome ('status'). It also qualifies the request as 'previously submitted' and requires a 'private status token', which distinguishes it from sibling tools like lab.request_capability without ambiguity.

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

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

The phrase 'previously submitted capability request' clearly indicates this is for follow-up polling after submission, and the need for a 'private status token' sets expectations for authorization. It does not explicitly state when to prefer this over siblings, but the polling semantics are clear enough for an agent to select it correctly.

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

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

Annotations only say the operation is not read-only, not idempotent, and not destructive, so the description must carry behavioral context. It does disclose that paid utilities are not executed directly but returned as an x402-compatible endpoint/payment requirement; however, side effects, failure modes, and auth expectations for free utilities are absent.

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, front-loads the primary behavior ('Invoke a free utility directly'), and includes the paid-utility exception without padding. 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?

No output schema exists and no return shape is given for the free-utility case, nor does the description mention how to discover valid slugs or that input is utility-specific. An agent is left with enough to attempt a call but not enough to reliably prepare a correct 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?

With only 33% schema description coverage (only idempotency_key is documented), the description needed to explain slug and input. It only implies that a utility is being invoked; it does not say how the slug identifies a utility, what input should contain, or how the two are related.

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 concrete action ('Invoke') and target ('utility'), and it adds an important free-vs-paid behavioral distinction. It is not a tautology and is distinguishable from siblings like list_tools/describe_tool, though 'utility' itself is left undefined.

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 when to call it (to directly run a free utility, or to obtain payment details for a paid one), but it does not explicitly explain when not to use it or point to lab.list_tools/lab.describe_tool for discovering valid slugs. Use is inferred rather than routed.

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, and destructiveHint=false, so the safety profile is covered. The description adds scoping to 'currently discoverable' utilities, but does not disclose output shape, pagination, or dynamic behavior. 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?

Two short sentences with no filler. The first sentence states the purpose and the second provides usage routing, making the description efficient 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 zero parameters, no output schema, and annotations already covering read-only/idempotent/non-destructive behavior, this description is sufficient to guide correct invocation. It clearly names what the tool returns and when to use an alternative instead.

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

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

The tool has zero parameters, and schema description coverage is 100%, so there is no parameter burden for the description to carry. The baseline of 4 applies because 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?

States a specific verb and resource: 'List all currently discoverable utilities.' The scope is clear and it is explicitly differentiated from search_tools, which is a sibling. The title reinforces the same purpose without ambiguity.

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?

Directly tells the agent when not to use this tool: 'Prefer search_tools when a concrete intent is known.' This is explicit routing guidance that helps select among siblings without leaving it to inference.

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

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

The description explains that this creates a private backlog request, which complements the annotations. It also adds useful data-handling guidance by warning against including secrets or personal data. It does not deeply cover submission behavior or response semantics, but the core mutation nature and privacy posture are clear.

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 with no filler. The primary purpose and usage condition are front-loaded, and the second sentence adds a concrete safety instruction that earns its place.

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

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

For a tool with eight parameters, a nested constraints object, and no output schema, the description is too thin to guide correct invocation. It does not explain how to fill required fields, what constraints mean, how evidence URLs should be used, or what happens after the request is submitted.

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, but it explains none of the eight parameters. The required fields problem, desired_outcome, and why_existing_tools_fail are not described, and the nested constraints object with retention options is entirely unaddressed. Parameter names are suggestive but the description adds almost no semantic value.

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

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

The description states a specific action ('Submit a free, private backlog request') and the condition under which it applies ('when no existing utility solves a concrete problem'). This clearly differentiates it from sibling tools that list, search, invoke, or describe existing tools.

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

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

The description gives a clear usage condition: use it only when no existing utility solves the problem. It also warns about what not to include (secrets, personal data, full conversation history). It does not explicitly name alternatives like lab.search_tools, but the intended routing is reasonably 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 provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by stating that the result is a 'compact machine-readable manifest,' signaling that the tool returns metadata rather than performing or invoking tools.

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 no filler. The main action and output type are front-loaded, and every clause 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?

For a simple search tool, the description covers purpose and return format, but it omits semantics for the optional category parameter and does not clarify behavior when no matches are found. Given the lack of an output schema, more detail about the returned manifests 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 0%, so the description must compensate. It explains the 'intent' parameter as a 'concrete intent' for searching, but it gives no guidance on the 'category' parameter, leaving its meaning and possible values entirely unspecified.

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 uses a specific verb ('Find') and a specific resource ('Agent Enhancer Utilities tools'), and clarifies the matching criterion ('a concrete intent'). It also states the output type ('compact machine-readable manifests'), which distinguishes it from sibling tools like lab.list_tools or lab.describe_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?

The description implies the tool is for finding tools from an intent, but it does not explicitly say when to prefer it over alternatives such as lab.list_tools or lab.describe_tool. There is no exclusionary guidance or mention of alternatives, so the agent must infer usage from the tool name and sibling list.

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