suggest_observations

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

With no annotations, the description carries the burden and does disclose meaningful traits: it mines existing session data rather than persisting anything, outputs suggestions that must be confirmed via create_entity, and lists the heuristics used. It does not mention whether anything is written, rate limits, or the candidate object's shape, keeping it short of a 5.

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?

Two sentences, both front-loaded: the first defines the mining behavior with concrete signals, the second gives the usage trigger and the follow-up tool. No filler or restated name/title.

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?

No output schema exists, so the description should signal the return shape; it indicates "observation candidates" and "salient suggestions" but does not describe the candidate object or count/pagination behavior explicitly. For a read-and-suggest tool it is largely complete, with a minor gap on return-value detail.

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 days, repo, and limit are fully documented in the schema (including defaults and the 50 cap). The description adds no parameter-level detail beyond what is already structured, so the 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 (mine recent session usage for observation candidates) and enumerates the exact signals it looks for (zero-hit packs followed by edits, hot files, error→fix sequences). It also names the sibling tool (create_entity) used to act on results, so an agent can place it in the workflow without opening schemas.

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?

Explicitly says when to invoke it ("at natural stopping points to capture what the session learned") and what to do next ("confirm salient suggestions via create_entity"). It lacks an explicit when-not/alternative condition, but the workflow guidance is clear.

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