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

With no annotations provided, the description carries the burden of disclosing behavior. 'Fetch' clearly implies a read-only operation with no side effects, which is useful. However, the description does not disclose limit/pagination behavior, response shape, or edge cases. These are not heavily problematic for a simple fetch tool, but they remain 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?

The description is a single sentence, front-loaded with the action 'Fetch' and the resource 'dataset examples', followed by a short purpose clause. There is no filler, redundancy, or repetition of schema details.

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 tool with no output schema, the description plus the input schema give an agent enough to understand the operation and purpose. It does not describe the exact shape of the returned examples, but the term 'examples' and the limit parameter make the return type reasonably inferable.

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% and the schema already documents dataset_id and limit, including the default for limit. The description adds no parameter-level semantics beyond the stated purpose. With full schema coverage, 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 clear action and resource: 'Fetch dataset examples'. The purpose clause 'for review or eval comparison' adds context and helps distinguish it from list_datasets, which presumably lists dataset metadata rather than examples. It does not explicitly differentiate itself from siblings like get_evaluation_summary, so it is clear but not fully 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?

The phrase 'for review or eval comparison' implies when an agent might want this tool, but it does not explicitly state when to use it over siblings such as list_datasets or get_evaluation_summary. The usage guidance is inferred from the purpose rather than stated as a decision rule.

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 add some behavioral context by revealing the kinds of aggregated metrics returned ('mean score, pass rate, etc.'), but it does not mention output structure, aggregation behavior, error conditions, or any other operational details. For a simple read-only lookup 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, focused sentence with no filler. The core action and key examples are front-loaded, and every word contributes to understanding the tool's purpose.

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 two-parameter tool with no output schema, the description provides a reasonable baseline: it names the action, the resource, and example metrics. However, it leaves gaps around what the return payload looks like, how the two parameters interact, and when this tool should be selected over sibling 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?

Schema description coverage is only 50%: eval_name has a helpful example but project_name has no description. The tool description does not compensate by clarifying either parameter's role or expected format, so the agent must rely on parameter names and general context.

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 identifies a specific verb ('Get') and resource ('evaluation summary'), and adds meaningful detail by naming the aggregated metrics ('mean score, pass rate, etc.'). However, it does not explicitly contrast itself with sibling tools like list_evaluations, so differentiation is mostly implicit.

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 intended use is implied by the phrase 'Get aggregated evaluation metrics' — an agent can infer this is for summary statistics rather than raw data. There is no explicit guidance about when to use this tool versus alternatives such as list_evaluations or get_spans, so the usage context is present but underdeveloped.

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 of behavioral disclosure. It reveals the return contents (input, output, latency) and 'Get' implies a safe read operation, but it does not mention ordering, pagination, errors, or the relationship between project_name and trace_id.

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?

One compact sentence that front-loads the verb and resource and avoids any redundant or filler content. Every word contributes to stating the tool's purpose and return data.

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 core purpose is clear for a simple two-parameter getter, but the description leaves project_name's role undefined and gives no guidance on selecting this tool over get_traces/query_traces. It is minimally viable but has notable contextual gaps.

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 covers trace_id with a description, but project_name has no schema description and the tool description does not clarify its role. With only 50% schema coverage, the description needed to compensate but instead adds no parameter meaning beyond the tool name.

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 ('Get') and resource ('individual spans') and explicitly lists the returned data (input, output, latency). This clearly distinguishes get_spans from sibling tools like get_traces, list_datasets, and list_evaluations by naming a distinct entity type.

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 explicit when-to-use or alternative guidance is given. The phrase 'individual spans' implies span-level retrieval, but the description never contrasts with get_traces or query_traces, so an agent must infer when this tool is the right choice.

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 transparency burden. It only says 'Retrieve', which implies read-only, but does not disclose output shape, ordering, pagination, or how filters affect results.

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 clear sentence with no waste. It front-loads the primary action and resource before mentioning filters.

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 input side is adequately covered by the schema, but no output schema exists and the description does not clarify return values. It also lacks sibling differentiation, so an agent may be unsure whether to choose get_traces or query_traces.

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 parameters are already documented. The description adds minimal semantic value beyond noting the existence of filters, which the schema already covers.

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 and resource: 'Retrieve LLM traces for a project' with optional filters. It distinguishes the tool by resource but does not explicitly differentiate it from siblings like query_traces or get_spans.

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 provided on when to use get_traces versus alternatives such as query_traces or get_spans. The phrase 'optional filters' is too generic to serve as usage direction.

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 disclosure burden. It states the operation but gives no information about return format, pagination, scope limitations, or any other runtime behavior beyond the literal listing action.

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. Every word contributes meaning, and it is appropriately sized for such a simple tool.

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 zero-parameter list tool, the basic operation is stated clearly enough to attempt a call. However, with no annotations, no output schema, and no guidance on how this relates to similar sibling tools, the description leaves some contextual ambiguity about what results and alternatives an agent should expect.

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 the schema coverage is 100%, so the baseline of 4 applies. The description does not need to explain parameter meanings because none 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 clearly states a specific verb ('List') and resource ('evaluation datasets in Phoenix'), making the tool's core function apparent. It is distinguishable from sibling tools like list_evaluations and list_projects by the resource type, though it does not explicitly contrast with get_dataset.

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_dataset, list_evaluations, or query_traces. The description only states what it does, leaving usage context entirely 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?

No annotations are present, so the description carries the behavioral burden. 'List' implies a read-only operation, but the description does not explicitly confirm no side effects, ordering, pagination, or return details. It is minimally transparent but leaves key behavioral traits unstated.

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 entire description is a single front-loaded sentence with no redundant words. It conveys the action, object, and scope 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?

For a one-parameter list tool with no output schema or annotations, the description covers the core purpose but omits return structure, filtering behavior, and relationship to named siblings. It is adequate but has gaps an agent would need to resolve from context or by invoking the 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%, and the description's 'for a project' adds semantic context that project_name is the project being queried. However, this mostly restates the parameter's name and does not explain accepted formats, required identifier types, or defaults.

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') with a clear resource ('evaluation runs and their scores') and scope ('for a project'). This distinguishes it from siblings like get_evaluation_summary, which implies a single summary object, and list_datasets, which targets a different resource.

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 'for a project' gives contextual usage, but there is no explicit guidance on when to choose this tool over get_evaluation_summary or list_projects. Usage is implied rather than stated with alternatives 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?

There are no annotations, so the description alone must convey safety and behavior. The verb 'List' implies a read-only operation and 'all' clarifies scope, but the description does not address pagination, return shape, or side effects; this is a modest gap for a trivial 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?

A single short sentence that is entirely relevant and front-loaded; no filler. It is appropriately concise for a tool with no parameters.

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 and the description provides the essential 'what' and the unfiltered scope. However, with no output schema and no annotations, the description remains a bare statement and leaves return-value details to inference, so it is minimal but viable.

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 has zero properties, so there are no parameter semantics to document. Per baseline for parameterless tools, the description does not need to compensate, although it adds no parameter detail.

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 a concrete resource ('Phoenix tracing projects'), making the tool's function immediately clear. It is distinct from sibling list tools because it targets projects, not datasets or evaluations.

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 conditions, alternatives, or when-not-to-use guidance. While 'list all' implies a use case, it never explains when to choose list_projects over sibling tools like list_datasets or list_evaluations.

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. It states that the tool runs a query, but does not mention whether it is read-only, whether results are paginated, whether there are limits, or what the query behavior is beyond filtering.

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 focused sentence with no wasted words. The core action and key filtering dimensions are front-loaded, making it 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?

With no output schema, no annotations, and an overlapping sibling set, the description leaves meaningful gaps: it does not explain what the tool returns, how it differs from get_traces, or what 'metadata filter' maps to. An agent would likely need to inspect sibling tools to make a reliable 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?

Schema coverage is 75%, so the schema already documents start_time, end_time, and status. The description adds a useful mapping by categorizing the filters as 'time range or metadata filter,' but project_name remains undocumented and the description does not clarify its role beyond what the schema implies.

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 a specific verb ('Run a structured query') and resource ('trace data'), and specifies filtering by time range or metadata filter. However, it does not differentiate from the sibling tool 'get_traces', which likely serves a very similar 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?

The description implies the tool is for querying traces with time or metadata filters, but it provides no explicit guidance on when to choose this tool over alternatives like get_traces or get_spans. No exclusions, prerequisites, or use-case boundaries are stated.

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