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

No annotations are provided, so the description carries the full burden. It discloses that rating='incorrect' moves a fact to disputed and that haki_context will never recall it again, that 'useful'/'irrelevant' only logs feedback without changing the fact, and that the effect is identical via the API path. It also mentions scope resolution via API key or HAKI_MCP_PROJECT_ID. This covers the key behavioral aspects, though it doesn't mention error handling or idempotency.

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 dense but not bloated. It front-loads the primary purpose and then details rating effects, target requirements, API equivalence, and scope. Every sentence contributes to understanding how to use the tool correctly. Slightly longer than minimal, but justified given the need to explain the conditional behavior.

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 complexity (4 parameters, no schema descriptions, no annotations), the description is complete enough for an agent to call the tool correctly. It covers what it does, how to select targets, scope resolution, and the API equivalence. An output schema exists, so return values are not required in the description. Missing details like error conditions are minor given the provided context.

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 0%, so the description must compensate. It explains the meaning of rating ('incorrect', 'useful'/'irrelevant'), fact_id (one precise fact), trace_id (one whole haki_context call), and implies the purpose of comment via 'logs feedback'. It adds meaningful context beyond the bare schema, covering all parameters except comment, which is adequately implied.

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 the tool's purpose: correcting memory from within a conversation. It specifies the exact behavior for each rating value ('incorrect' moves a fact to disputed, 'useful'/'irrelevant' logs feedback) and distinguishes it from siblings by focusing on feedback/correction rather than capture, recall, or forgetting. This is a specific verb-resource pairing with clear scope.

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 provides explicit usage conditions: exactly one target required (fact_id for a precise fact, trace_id for a whole haki_context call). It also explains the effect on haki_context (won't recall disputed facts) and mentions equivalence to POST /v1/feedback. It doesn't explicitly contrast with sibling tools like haki_forget, but the mechanism is clear enough for an agent to infer when to use it.

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