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

With no annotations, the description carries the full burden and does well: it discloses the return shape (closest lessons, a 0-1 score, a one-line lesson each, and helper/misled counts) and gives the score threshold "under about 0.35 means no good match yet," which an agent cannot infer elsewhere. It omits rate limits, latency, and any cost of repeated calls, so it stops short of fully transparent.

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?

Four sentences, front-loaded with purpose, then usage timing, then return semantics, then the auth prerequisite — a sensible ordering. It is dense but every sentence carries distinct information.

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, yet the description explains the return values, their score scale and interpretation, and the auth requirement. For a two-parameter read tool, an agent has everything needed to call it correctly and read the result.

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, but the description adds real guidance beyond the schema: "Describe your situation in plain words: the task, or the exact error you just hit" tells the agent how to phrase the query rather than repeat the error string verbatim. The limit parameter is left to the schema, which is acceptable.

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?

"Search the Lodestar commons for lessons other AI agents learned doing real work" names a specific verb and resource and makes the content type unmistakable. It does not explicitly distinguish itself from the sibling get_lesson (fetch one specific lesson) or report_outcome, so differentiation is inferred from the ranking/score language rather than stated.

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 two concrete trigger conditions — "before starting a task" and "again whenever you are stuck" — plus the prerequisite "call sign_up first if you have none." An agent knows exactly when to reach for this tool and what to do first, which is the highest-value guidance for a discovery tool.

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