list_distinct_values

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

No annotations are provided, so the description carries full burden. It correctly indicates read-only behavior and ordering by frequency. However, it omits details like output format (e.g., whether counts are included) and any constraints on field selection.

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 concise and well-structured, with a clear opening sentence followed by bullet-pointed use cases. Every sentence adds value without redundancy.

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?

While the description provides good usage examples, it is incomplete regarding output format (no output schema) and does not explain the structure of the returned data. For a data exploration tool, this is a notable 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 coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for the 'field' parameter (name, packageName, environment, outputValue.statusCode), explaining its purpose beyond the schema. No significant additional detail for other 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 'List unique values for a field, ordered by frequency' and provides concrete use cases (discovering endpoints, instrumentation packages, environments, JSONB values). This distinguishes it from sibling tools like query_spans or aggregate_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?

The description explicitly lists when to use the tool (e.g., discovering available endpoints) and implies usage before building specific queries. However, it does not name alternative tools for different scenarios, leaving a slight gap in guidance.

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