market_test

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

With no annotations, the description carries the full burden and does disclose the approval gate, persona-creation pause, simulation reuse, and background execution. It omits spend/cost implications, permissions, idempotency, error behavior, and how results are retrieved.

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 purpose and approval workflow are front-loaded, and most content is necessary given the empty config schema. However, the long run-on config key list is dense and hard to scan, reducing practical clarity.

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 asynchronous job with no annotations, no output schema, and an empty config schema, the description provides useful config semantics and the approval sequence. It still omits required/default config keys, result retrieval via job_status/results, and error or cost behavior.

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 schema has 0% description coverage and exposes only an undifferentiated config object, so the description's enumeration of keys and their meanings compensates substantially. Some nested structures are only loosely described, but the key semantics are much richer than the schema alone.

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 opening phrase 'All-in-one market test' plus the config-key list makes clear that this tool runs persona generation, feed creative testing, surveys, panels, and reporting. However, it does not explicitly distinguish itself from siblings such as run_test, website_test, or email_test.

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?

It gives a concrete two-step approval flow: call without approve_spend to stop after persona creation, then call again with simulation_id and approve_spend true. It also notes that the work runs as a background job. It lacks guidance on when to choose this over sibling test tools.

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