run_scenario

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

With no annotations, the description carries the full behavioral disclosure burden, and it does so thoroughly. It reveals the async 202/pending behavior, the need to poll until completion, the NaN failure mode, the rebuild_model requirement for API-initiated scenarios, and the performance implications of skip_slicing. No annotation contradiction exists.

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?

Though lengthy, the description is efficiently organized: a one-line summary, a critical NaN warning front-loaded, an async note, a compact workflow, then per-parameter details. Every sentence adds necessary information, and headings/separators make the size navigable for an agent.

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 complex async tool with no output schema and zero schema-level parameter help, the description is remarkably complete. It covers prerequisites, input format, parameter semantics, failure conditions, async polling, and the intended workflow. An agent has nearly everything needed to call the tool correctly and interpret the follow-up step.

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?

The input schema has 0% property description coverage, so the description must compensate for all seven parameters. It does this completely: model_hash is explained, scenario_data has a concrete example and format details, spend_metadata shows the exact per-entry structure, and each boolean parameter gets its meaning and default. This is exemplary parameter documentation.

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 uses a specific verb-resource pairing: "Run a what-if scenario prediction on a completed model." It clearly distinguishes this from siblings by naming the workflow (get_scenario_template -> run_scenario -> get_scenario_results) and by clarifying that channel names come from the template/results, not from create_model.

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 gives strong usage context: use get_scenario_template first, then modify values, then run_scenario, then poll get_scenario_results. It also states the async polling requirement. It does not explicitly say when not to use run_scenario versus a sibling like run_optimizer, so it misses the full 5, but the workflow guidance is clear and actionable.

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