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

With no annotations, the description must carry the behavioral burden. It discloses that this is a polling/check operation for async work and describes the return shape ('done' and eventually 'response' payload). It doesn't mention side effects, but as a read-only check, this is adequate context beyond the schema.

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 zero wasted words: the first states the purpose, the second gives usage and return behavior. The most critical information (when to use and what to pass) is front-loaded. No redundancy with the schema or sibling list.

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 polling tool with no output schema, the description covers the key aspects: when to use, what to pass, and what the response contains. It doesn't explain how to interpret 'done' or polling frequency, but that is reasonably inferred. Adequate and nearly 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 coverage is 100% and the schema already explains the 'path' parameter with an example. The description adds 'pass the path from that response', which reinforces the source but doesn't introduce meaning beyond what the schema provides. Baseline 3 is appropriate.

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 ('Check on') and resource ('long-running analytics query'), and distinguishes itself from siblings by referencing query_metrics and list_dimension_values. It is clear what the tool does and how it differs from the listed 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 explicitly tells when to use the tool: 'when query_metrics or list_dimension_values returned done: false' and instructs to pass the 'path' from that response. It does not explicitly state when not to use it or name alternatives, but the 'when' condition is exact and actionable.

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 burden of behavioral disclosure. It does disclose the return shape and the read-only intent ('Discover'). However, it does not mention that slow queries may return a pending operation with done:false and a path for get_operation, even though pollTimeoutSeconds references this behavior. This is a notable behavioral gap.

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 three sentences: purpose with examples, downstream usage, and return shape. It is front-loaded, concise, and every sentence earns its place without unnecessary 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?

With 9 parameters and no output schema, the description needs to cover the result contract adequately. It provides the primary return shape, but omits the asynchronous pending-operation case (done:false / path / get_operation), which is relevant for correct invocation and result handling. Since the schema documents pollTimeoutSeconds, this is not fatal, but it leaves a 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?

Schema description coverage is 100%, so the baseline is 3. The description adds useful context about dimension value examples and how values feed query_metrics filters, but it does not add substantial parameter-specific meaning beyond what the schema already provides.

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 and resource: 'Discover the actual values a dimension takes for a metric in a universe over a date range.' It gives concrete examples (Country codes, Platform names, FunnelName values, Place IDs), which clearly distinguish it from sibling tools like list_metrics, query_metrics, and get_operation.

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 tells the agent how to use the output: 'Use the returned values in query_metrics filters or to understand breakdown results.' This gives clear context for when the tool is valuable. It does not explicitly contrast with list_metrics or state when not to use it, so it falls slightly short of a 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?

With no annotations, the description carries the behavioral burden and does well: it discloses case-sensitivity, the exact fields returned, and the fact that unfiltered output may be large. It does not mention pagination or rate limits, but those are minor for a metadata-listing tool.

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 with the main purpose front-loaded and no wasted words. Every clause adds operational value: exact output fields, first-step usage, and filtering guidance.

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 two optional parameters and no output schema, the description fully explains what the response contains and why the agent should call it first. The sibling relationship to query_metrics is implied clearly enough for correct tool selection.

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 already documents both parameters fully, including case-insensitive search behavior and an explicit category enum. The description only restates 'filter by category or search by keyword,' adding no meaningful parameter-level detail 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 uses a specific verb ('List') and resource ('analytics metrics'), then enumerates the exact contents returned: name, label, category, granularities, retention, and dimensions. It also positions the tool as the discovery entry point, distinguishing it from the sibling query_metrics 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 explicitly instructs 'Call this first to pick a metric and check which granularity and dimensions it accepts,' which is clear when-to-use guidance. It also advises using filters to keep output small, but it does not explicitly name when-not-to-use or alternate sibling 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?

With no annotations present, the description carries the full burden and does so thoroughly. It discloses async polling behavior, the done:false protocol, the shape of returned data points, possible status values, and the required API scope. This is far beyond a minimal description and gives the agent reliable expectations for 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 compact four-sentence paragraph with no filler. It front-loads the core purpose, then covers return shape, async behavior, and authentication requirements in logical order. Every sentence contributes meaningful information.

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—9 parameters, no output schema, and no annotations—the description is remarkably complete. It explains what the response contains, how slow operations work, how to recover a pending operation, and what permission is required. There is little an agent would need to infer or discover 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?

The input schema already provides 100% parameter coverage with detailed descriptions for every field, so the baseline is 3. The description adds high-level context about bucketing, breakdowns, and filtering, but it mostly restates what the schema documents rather than adding new parameter-level semantics.

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: 'Query a time series for one analytics metric of a Roblox universe.' It lists concrete metric examples and differentiates itself from siblings like list_metrics, list_dimension_values, and get_operation by focusing on actual data retrieval and even explaining when to delegate to get_operation.

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 clearly establishes when to use the tool: any time you need metric time-series data for a universe. It also provides an explicit routing rule for slow queries ('if the response has done: false, call get_operation'), but it does not explicitly state when to prefer list_metrics or list_dimension_values over this tool, though the schema does mention them.

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