list_llm_tools_tool

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

With no annotations, the description carries the full behavioral burden but only states the output format. It does not disclose whether the operation is read-only, whether it has side effects, permissions required, or performance characteristics beyond the limit parameter.

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 well-structured, front-loading the purpose, then listing args and returns. It is appropriately sized, though the Returns section is somewhat redundant given the output schema exists.

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 simplicity (5 optional params, output schema exists), the description covers purpose, parameters, and return format adequately. The main omission is usage guidance, but the core information needed to invoke the tool is present.

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%, so the description must compensate. It provides meaningful details for each parameter, including ISO 8601 examples for start_time and end_time, filter semantics for service_name and gen_ai_system, and the default and purpose of limit.

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 ('List all LLM tools being used') and explains the identifying mechanism ('traceloop.span.kind == tool'). It clearly distinguishes this from sibling tools like list_llm_models or search_traces by focusing on tool/function calls made by LLM applications.

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 explicit guidance on when to use this tool versus alternatives such as list_llm_models or get_llm_usage. The purpose implies usage, but no conditions or exclusions are stated.

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