invariance_run_llm_calls

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

The annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds valuable behavioral details: 'append order' and 'paginated' indicate the iteration pattern, and the enumerated fields (model, tokens, cost, latency, node_id) set expectations for response content. No contradiction with annotations.

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 concise sentences with zero waste. The primary action is front-loaded, and the second sentence lists the included data fields. Every word earns its place.

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 paginated list tool with no output schema, the description provides the essential details: ordering, pagination, and the fields each entry contains. It doesn't explicitly state that responses include a next_cursor for subsequent pages, but this is strongly implied by the cursor parameter and 'paginated'. Overall, it is sufficient for an agent to call it correctly.

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 33% (only cursor has a description). The description mentions 'paginated' which hints at the roles of limit and cursor, but it does not explicitly explain run_id or limit. While the parameter names are self-explanatory, the description does not compensate enough for the low coverage, though it adds marginal 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 states a specific verb ('List'), a precise resource ('LLM calls for a run'), and adds ordering ('append order') and pagination. It clearly distinguishes from siblings like invariance_run_get (run details) and invariance_run_metrics (run-level metrics) by specifying exactly what it returns and the included fields.

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 provides clear context for when to use this tool: when listing LLM calls for a specific run. It does not explicitly mention alternatives or exclusions, but the specificity of 'LLM calls for a run in append order' effectively communicates its niche among the many run-related tools, without needing to name them.

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