memory_lesson_search

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

Annotations already cover the safety profile (readOnly, non-destructive), but the description adds substantial beyond-annotation context: lessons describe their source run rather than the current one, polarity flags dead-ends, and re_verify marks entries whose facts have changed. The anchoring warning is a genuinely useful behavioral caveat.

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?

Purpose, timing, application caveats, and return shape are ordered sensibly with the action front-loaded. It is a little dense and tangential in the middle (the anchoring/stale-framing aside), but nearly every sentence carries meaning.

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?

With a real output schema present, the description plausibly does not need to spell out return values, yet it still enumerates the entry fields (polarity, re_verify, confidence, etc.), which helps interpretation. Coverage of how to read results is complete; pagination/limits are handled by top_k in the schema.

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 100%, so query, top_k, and verbose are already documented. The description adds only a loose restatement of query ('similarity to the task at hand') and no extra syntax or format guidance, so the baseline of 3 applies.

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?

States a specific verb (search) and a specific resource (learned lessons / procedural memory), which cleanly separates it from the general-memory siblings like memory_search, memory_recall, and memory_world_search. An agent can identify the memory category without opening the schema.

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?

Gives concrete when-to-call guidance ('Call at the START of a task') and how to apply results (apply where today's specifics match, heed polarity '-' entries, re-derive re_verify entries). It stops short of naming a specific alternative sibling or stating when NOT to use it, so it is strong but not exhaustive.

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