ecosystem_deep_review_request_batch

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

With no annotations provided, the description carries full responsibility and does disclose the mutable side effects: it creates an EcosystemDeepReview row per candidate. It also clarifies the division of responsibility ('Leader is responsible for actually spawning each agent via the Agent tool'), making the batch-nature and delegation behavior transparent beyond what schema reveals.

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 well structured with a clear Stage-1 label, distinct sentences covering pipeline inputs, side effects, and downstream handoff. Every sentence earns its place; there's no filler or redundancy. It is front-loaded with the primary purpose.

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?

The tool is complex (orchestration, batch creation, sub-agent spawning) but the description covers the essential flow: candidate selection criteria, row creation, output payload type, leader responsibility, and downstream write-back tool. Combined with a 100% schema coverage and an existing output schema, this is complete for an orchestration tool of this complexity.

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% so the baseline is 3. The description adds value by clarifying the AND-tag semantics and noting empty list returns 400 (behavioral constraint not obvious from schema alone). The description reinforces that research_goal is injected into each sub-agent prompt, though the schema already says this. Slight redundancy, but the tag semantics clarification pushes it above baseline.

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+resource: 'Queue architecture-analysis dispatches for tag-filtered candidates' and describes the pipeline with an explicit Stage label. It clearly identifies inputs (active+shallow_done profiles with AND tag semantics), the output (DispatchIntent payloads), and differentiates this from sibling ecosystem tools by naming the downstream write-back target (ecosystem_apply_architecture_md).

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 clear operational context: it pulls specific profile states (active+shallow_done), uses AND tag semantics, and names the downstream tool each agent calls. However, it does not explicitly state when NOT to use this versus alternatives like ecosystem_deep_review_request (singular), ecosystem_scan, or ecosystem_trigger_debate, leaving some comparison ambiguity.

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