simulatte_run_study

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

No annotations exist, so the description must carry the full burden. It hints at asynchronous behavior by mentioning 'poll', but does not disclose whether the operation is destructive, requires authentication, has rate limits, or how inputs are validated. The description is insufficient for a mutation tool.

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 sentences, front-loads the action and resource, and includes a concrete follow-up action (poll with simulatte_get_results). There is no extraneous information; every sentence is purposeful.

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 absence of an output schema and the complexity of nested inputs, the description provides basic context: it returns a study_id and directs polling. However, it omits details about error handling, required permissions, cost implications, and the dependency of inputs on the chosen SKU, leaving the agent partially informed.

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 75% (three of four params have schema descriptions). The description adds context for the sku param by listing example study types, but does not clarify the structure of 'inputs' beyond 'SKU-specific'. This provides marginal added value over the schema.

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 that the tool runs a Simulatte synthetic research study and returns a study_id, with examples of SKU categories. It distinguishes from siblings by mentioning simulatte_get_results for polling, but does not explicitly differentiate from other related tools like simulatte_estimate_cost.

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 usage by noting that the returned study_id can be polled with simulatte_get_results, providing a chaining hint. However, it lacks explicit guidance on when not to use this tool or clear alternatives like simulatte_estimate_cost for cost estimation before running.

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