Stage/apply AI candidate flows

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

Annotations only say readOnlyHint=false, destructiveHint=false, openWorldHint=true; the description goes well beyond that by disclosing both write targets (.orangepro/flows.json on generate, analysis.candidate_flows on apply=true) and an explicit safety guarantee that candidates never affect Proven counts, tiers, or coverage. The remaining gap is that openWorldHint=true is never explained — the BYOK provider/model overrides imply remote LLM calls with auth and cost implications that are not surfaced.

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?

Three dense sentences with the key framing ('Opt-in AI lane') front-loaded and the safety guarantee placed last, which is the right order. The jargon ('CodeSymbol ids', 'survivors', 'Proven') makes it slightly heavier than necessary, but no sentence is wasted.

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?

There is no output schema, so the description should say what comes back — a list of proposed chains with ids to verify? — and it doesn't. For an open-world tool that also writes files, the absence of any note on provider/API-key prerequisites or expected return shape leaves real gaps, though the write semantics and non-impact guarantee cover the most important 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?

Schema coverage is 100%, so all four parameters are already documented in structured data, and the description mostly restates the apply behavior that the schema already spells out. It adds no guidance on model/provider selection, defaults, or workspace scoping beyond what is in the schema, so baseline 3 is correct.

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 and resource — proposing candidate behavior-flow chains over existing deterministic entries and CodeSymbol ids — and clearly marks this as an opt-in AI lane distinct from the deterministic one. It is clear and distinguishable in substance, but it never names the closest sibling (orangepro_ai_links) so the agent must infer the boundary from the phrase 'behavior-flow chains' vs. links.

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?

'Opt-in AI lane' plus 'Candidate flows are a verify-these worklist' implies when this tool is appropriate: an exploratory, non-authoritative pass. However, with 17 siblings including orangepro_ai_links, orangepro_prove, and orangepro_update_graph, there is no explicit statement of when to choose this over those alternatives or when not to use it at all.

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