knitbrain_metrics

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

No annotations are provided, so the description bears full responsibility. It does not disclose whether the operation is read-only, requires permissions, has side effects, or other behavioral traits. While 'telemetry' suggests a safe query, no explicit assurance is given.

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?

A single sentence that front-loads the key term 'Compression telemetry'. It is concise with no redundant words, though the parenthetical 'TOIN self-tuning' is somewhat cryptic and may require domain knowledge.

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 zero parameters and no output schema, the description covers the overall topic but lacks details on output format, possible values, or how to interpret metrics. It is minimally complete for a simple query tool but could be enhanced with return type hints.

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?

The tool has zero parameters, and schema description coverage is 100% by default. The description adds meaning beyond the schema by specifying the kind of data returned (tier counts, retrieval rates), which helps an agent understand output semantics even without an output schema.

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 it provides 'compression telemetry' including 'recall-store tier counts' and 'per-kind retrieval rates', which is a specific verb-resource mapping. It distinguishes from siblings like 'knitbrain_context_meter' by focusing on metrics, but could be more precise about the resource type.

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?

No explicit guidance on when to use this tool versus alternatives like 'knitbrain_context_meter' or 'knitbrain_get_learning'. The description implies it is for telemetry retrieval but does not state when not to use it or provide comparisons.

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