Agent Enhancer Utilities

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=False. The description adds context about what specific information is returned, such as schema, price, limits, side effects, retention, and HTTP endpoint, which is valuable beyond the safety hints. It does not describe error handling or rate limits, but the annotation coverage is sufficient.

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 filler. It efficiently communicates the tool's purpose and output scope in 15 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?

For a simple one-parameter read-only tool, the description covers the main output fields and the safety profile is covered by annotations. However, the ambiguity around the 'slug' parameter prevents full completeness, and there is no mention of response format or error conditions, though these are less critical.

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

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

The schema has zero description coverage, and the description does not explain the 'slug' parameter. It only refers to 'one utility', leaving the agent to infer that slug is the identifier. There is no guidance on how to obtain or format the slug, which 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 uses the specific verb 'Get' and clearly enumerates the exact data points returned (schema, price, limits, side effects, retention, HTTP endpoint) for a single utility. This differentiates it from siblings like list_tools and search_tools by focusing on one utility's detailed metadata.

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 specifications of a single utility, but it does not explicitly mention alternatives or state when not to use this tool. There is no comparison with list_tools or search_tools, leaving the usage context 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, idempotentHint=true, and destructiveHint=false, so the description doesn't need to restate safety. It adds context beyond annotations by explaining the 'poll' semantics (likely repeatable, non-mutating) and mentioning the 'private status token', implying authorization requirements. This is useful but could go further with error/rate-limit 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?

The description is a single, concise sentence that front-loads the core action ('Poll') and includes the essential context in the fewest words possible. 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?

With only two parameters, strong annotations, and no output schema, the description covers the essential use case well: polling a prior request with a token. It doesn't describe the return payload, but the title ('Get capability request status') and the verb 'poll' imply a status response. This is adequate for a simple polling tool, though slightly more detail on the response would be ideal.

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 provides no descriptions for request_id or status_token (0% coverage). The description suggests that the tool polls a request using its private status token, which maps loosely to the two parameters, but it doesn't explicitly name them or explain their exact roles/constraints. The description adds some meaning but doesn't fully compensate for the schema 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 uses the specific verb 'poll' and clearly identifies the resource as 'a previously submitted capability request'. It also mentions the key input ('private status token'), which distinguishes it from the sibling tool 'request_capability' that presumably creates requests.

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 implies this tool is used after a request has been made, and the action 'poll' suggests repeated status checks. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a full 5.

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

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

The description adds the payment behavior beyond annotations, explaining the x402 flow for paid utilities. It doesn't discuss side effects or auth requirements, but annotations already declare non-read-only and non-destructive, so the added context is meaningful.

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

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

Two sentences, front-loaded with the primary action, and second sentence adds the paid-utility edge case. No fluff.

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

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

The description covers the free/paid distinction but omits return-value structure and how to discover valid slugs. With no output schema, more detail on expected response would improve completeness, though the sibling search/list tools mitigate the discovery gap.

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 3 parameters with only 33% description coverage; the description does not mention slug or input, leaving the agent to infer their roles. For an invoke tool, explicit guidance on how to provide input would be valuable but is missing.

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 invokes a free utility and handles paid utilities by returning an HTTP endpoint. This distinguishes it from sibling tools like describe_tool and list_tools, which are metadata-focused.

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 context for when to use it: for free utilities, invoke directly; for paid, handle payment via endpoint. It doesn't explicitly exclude alternatives, but the sibling set doesn't include another invocation tool, so usage context is clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds minimal context with 'currently discoverable,' implying a dynamic list, but does not describe output format or other behavioral traits. This is adequate but not rich.

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: 'List all currently discoverable utilities. Prefer search_tools when a concrete intent is known.' Every word earns its place, with 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?

This is a simple, zero-parameter tool with strong annotations and no output schema. The description clearly states the purpose and provides an alternative usage tip. For its complexity, the description is fully complete; return values are adequately implied by 'List.'

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, so schema coverage is 100% (vacuously). Per the rubric, the baseline for zero parameters is 4. The description adds no parameter-specific semantics because there are none to describe.

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: 'List all currently discoverable utilities.' It also distinguishes from sibling tools by saying 'Prefer search_tools when a concrete intent is known,' making it clear this is the general listing tool, not the intent-based search 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 explicitly provides usage guidance: 'Prefer search_tools when a concrete intent is known.' This tells the agent exactly when to use an alternative, satisfying the when/when-not requirement with a named sibling.

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?

All annotations are false, so the description carries the burden. It adds that the request is 'free' and 'private', which is useful context about the nature and privacy expectations. However, it doesn't disclose what happens after submission (e.g., whether a tracking ID is returned, whether the request is actually seen by humans, or any rate limits). It provides some transparency but not rich behavioral detail.

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 exactly two sentences: the first states the purpose and condition, the second provides safety guidelines. It is front-loaded with the verb and resource, and every sentence adds meaningful information without redundancy. This is a model of concise, efficient writing.

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

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

With 8 parameters (including nested objects), required fields, and no output schema, the description is far too brief to be complete. It doesn't explain how to construct a valid request, what the response format will be, or any expectations about the outcome. The constraints are only in the schema (e.g., maxLength, URL limits), but the description doesn't synthesize them into practical guidance.

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%: none of the eight parameters have descriptions in the schema, and the tool description does not compensate. It doesn't explain the meaning or format of 'problem', 'desired_outcome', 'why_existing_tools_fail', or the nested 'constraints' object. The parameter names are somewhat self-explanatory, but the description adds zero value in explaining how to fill them, which is a significant gap for a tool with 8 parameters.

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 ('Submit a free, private backlog request') and the specific resource (a capability request). It also distinguishes from sibling tools by specifying the trigger condition ('when no existing utility solves a concrete problem'), making it evident this is for requesting missing functionality rather than describing, listing, searching, or invoking 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 when-to-use condition: 'when no existing utility solves a concrete problem.' It also provides explicit prohibitions on content ('Do not include secrets, personal data, or full conversation history'). It doesn't explicitly name alternatives like 'search_tools' first, but the condition implies checking existing tools, so it's strong but not fully exhaustive.

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 establish that the tool is read-only, idempotent, and non-destructive, so the description does not need to restate those. It adds useful context by specifying the return format ('compact machine-readable manifests'), which is valuable since there is no output schema. 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 exactly two sentences with no filler. The purpose is front-loaded, and the return behavior is stated concisely. 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?

For a simple search tool with only two parameters and no output schema, the description provides adequate but minimal guidance. It would benefit from explaining what a 'manifest' contains or clarifying the role of the 'category' parameter. Overall, it is functional but leaves gaps in 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%, so the description must explain parameter meaning. It only hints at the 'intent' parameter ('match a concrete intent') and says nothing about the optional 'category' parameter. This leaves half the parameters undocumented, failing to compensate for the schema coverage 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?

Clearly states the tool's function: finding tools based on a concrete intent. The verb 'find' and resource 'tools' are specific, and it is distinguished from sibling tools like list_tools and describe_tool by focusing on intent-based matching rather than enumeration or direct description.

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 the user has a specific intent, but it does not explicitly state when to use this tool versus alternatives like list_tools or describe_tool. No exclusions or alternative guidance are provided.

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