List LLM Tools

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false bir, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it reveals the internal filtering mechanism (traceloop.span.kind == tool) and the aggregation behavior (grouped by tool name with usage statistics), which helps the agent understand what the result represents.

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 short sentences, with the core purpose front-loaded and no filler. The grouping/usage-statistics detail earns its place and is not redundant with the schema or annotations.

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 relative simplicity, the output schema presence, and fully described parameters, the description is mostly complete. It explains the key behavioral detail (span kind filtering) and the return shape (grouped with usage statistics). The only gap is explicit guidance on when to choose this over related 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 100%, so every parameter already has a clear description. The tool description does not add parameter-specific semantics beyond what the schema provides, which fits the baseline of 3 for full schema coverage.

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 names a specific verb and resource ('List all LLM tools being used'), identifies the exact span condition (traceloop.span.kind == tool), and states the output grouping ('grouped by tool name with usage statistics'). This clearly distinguishes it from sibling tools like list_llm_models.

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 when to use the tool ('Discovers which tools/functions LLM applications are calling'), but it does not explicitly compare it to alternatives such as list_llm_models or get_llm_usage, nor does it state when not to use it. Usage guidance is present but only implicit.

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