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

No annotations provided, so description carries full burden. It explains the tool persists data (trace, duration, status, tokens, cost) for later analysis. However, it does not disclose side effects, authentication needs, rate limits, or whether it's safe to call multiple times. Adequate but not thorough.

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, concise and front-loaded: first explains what it does, second explains the benefit. No wasted words. Length is appropriate for the complexity.

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?

Tool has 16 parameters but no output schema. Description does not explain what the tool returns (e.g., success indication, span ID, or error). Also does not clarify the 'response_format' parameter or how the nested 'attributes' object works. Given the parameter count and complexity, more detail is needed.

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% (all 16 parameters have descriptions), so baseline is 3. The description adds context by listing operation types (model call, tool call, etc.) which align with the 'kind' enum. No additional detail beyond the schema, but no omission either.

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 the tool logs spans of AI agent execution (model calls, tool calls, retrieval, internal steps). It distinguishes itself from sibling tools like obs_trace_get, obs_events_search, etc., which are for querying/analyzing, not logging.

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 (when recording events), but does not explicitly state when not to use or mention alternative sibling tools. Usage context is implicit, not directive.

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