UsageMax public observability

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

Annotations already signal a read-only, non-destructive operation. The description adds useful behavior beyond that: responses include 'cited public resources' and the question is 'bounded', implying the tool does not roam into unrelated topics. This is meaningful context without contradicting 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 with no filler: it names the action, resource, scope, and return behavior. Every phrase earns its place, and the key constraint 'bounded' 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?

With one simple parameter, an output schema, and annotations covering the safety profile, the description is complete for what an agent needs to invoke this tool. It explains what to ask and what to expect back (cited public resources), while relying on the output schema for return-value details.

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 only the type, length limits, and required flag for 'query', with zero description coverage. The description at least clarifies that the query should be a 'natural-language question' about UsageMax, but it gives no examples or guidance on phrasing or scope boundaries. This is adequate but minimal.

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 ('Ask'), the target resource ('UsageMax documentation'), and a key characteristic ('bounded natural-language question', 'cited public resources'). It is distinct from the sibling data-retrieval tools, which focus on leaderboard, network stats, and public profile data.

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 'bounded natural-language question about UsageMax' gives clear context that this tool is for documentation Q&A rather than for pulling metrics or profiles. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for an agent to route 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 already declare readOnlyHint=true and destructiveHint=false. The description adds that the leaderboard is public (implying no auth) and capped at 100 rows, which are useful behavioral details 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?

A single sentence with no filler. The key constraint (maximum 100 rows) is front-loaded, meeting the conciseness standard perfectly.

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 read-only tool with 2 optional enum parameters and an output schema, the description plus schema and annotations fully cover what an agent needs. It lacks nothing essential for 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?

Schema coverage is 100% with full descriptions for both 'metric' and 'period' parameters, and the description adds no additional parameter detail. The baseline of 3 applies.

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

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

The description states a specific action ('Read') and resource ('public UsageMax leaderboard'), adding a row-limit constraint. This clearly distinguishes it from siblings like network_stats or public_profile, and an agent can tell what it does without opening the schema.

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 accessing the leaderboard, and the sibling names (ask_site, network_stats, public_profile) do not overlap, so an agent can infer when to use it. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough.

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=false, and destructiveHint=false. The description adds useful behavioral context beyond those: the data is aggregate, public, sourced from UsageMax, and the tool may render an inline observability view. No contradiction exists.

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 with no redundancy. It front-loads the core action and resource, then adds the optional rendering behavior. Every phrase carries meaning and the structure is easy to scan.

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 that there are no parameters, an output schema exists, and annotations cover safety, the description is nearly complete for invocation. The only missing piece is clearer usage guidance relative to sibling tools, but that is captured under usage_guidelines and does not block a correct call.

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 the schema provides complete coverage by definition. The baseline for no-parameter tools is 4, and the description does not need to add parameter meaning. It correctly avoids inventing parameter details that do not exist.

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 ('Read'), a clear resource ('aggregate public UsageMax network statistics'), and an additional behavior ('render the optional inline observability view'). This is specific enough to distinguish it from siblings like leaderboard or public_profile without opening the schema.

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 no guidance on when to choose this tool over the sibling tools, nor does it mention any exclusions or alternative conditions. For a simple read-only stats tool this is a moderate gap, but the lack of any comparison to ask_site, leaderboard, or public_profile means the agent must infer 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 establish read-only and non-destructive behavior. The description adds meaningful context beyond the annotations by disclosing the 'opt-in public' access constraint and noting that the result includes aggregate statistics. This clarifies privacy/availability expectations without contradicting 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 one compact, front-loaded sentence. It communicates the resource, access constraint, and result type without any filler or redundant restatement of the title.

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 one-parameter, read-only tool with full schema documentation, safety annotations, and an output schema, the description is sufficient. It covers the essential access constraint ('opt-in public') and the general response shape ('aggregate statistics'), so an agent has what it needs to invoke 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?

The input schema fully describes the only parameter, including the '@ prefix' requirement, so the description does not need to add parameter-level detail. The description adds no new meaning about handle, but schema coverage is 100%, matching the baseline expectation.

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 ('Read'), a named resource ('UsageMax profile'), and key qualifiers ('one', 'opt-in public'), making the operation clear. It doesn't explicitly contrast with sibling tools like leaderboard or network_stats, but the singular and public scope is enough to identify the core purpose.

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 about when to use this tool versus its siblings, no exclusions, and no mention of alternatives. The sentence states what the tool does but not the selection criteria an agent would need to choose it confidently over ask_site, leaderboard, or network_stats.

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