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, non-idempotent, closed-world), so the description earns credit for adding what annotations cannot: the 'apply where today's specifics match' caveat against stale framing, and the meaning of re_verify as flagging lessons whose subject facts changed. It doesn't discuss scoring, ranking, or rate limits, keeping it at a 4.

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?

Core instruction ('Call at the START of a task') is front-loaded ahead of interpretation guidance, and every sentence carries operational content. The backtick-laden prose is slightly dense and the Returns block is arguably redundant given a real output schema, but there is little true waste.

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 search tool with full schema coverage and an output schema, the description covers timing, result interpretation, polarity semantics, and the re_verify caveat — everything an agent needs to use it correctly. Including the return shape is redundant rather than missing, so completeness is high but not perfect.

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/verbose are already documented in the schema, and the description adds no input-parameter meaning beyond them. The 'Returns' block describes output fields rather than inputs, so baseline 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 and resource ('Search learned lessons (procedural memory) by similarity to the task at hand'), and the '(procedural memory)' qualifier separates it from the general memory_search/memory_recall siblings. An agent can tell what it will get back 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?

Explicit timing guidance ('Call at the START of a task') plus concrete usage rules ('Heed polarity - entries — known dead-ends'). It tells the agent when to run it and how to interpret results, though it doesn't name a competing sibling tool for contrast.

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