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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals what content is returned — learned weights and conversion stats — but does not mention whether the operation is read-only, whether it has side effects, how data is refreshed, or any access constraints. This leaves important behavioral traits undisclosed.

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?

The description is a single compact sentence with no filler. The core concepts — learned weights, per-signal-type granularity, and outcome-based conversion stats — are front-loaded and every phrase earns its place.

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 zero-parameter tool this is minimally adequate, but with no output schema and no annotations, the description should clarify more about the returned data, such as format, time window, or how conversion stats are measured. It names the two main content areas but leaves the agent without concrete details about what a response will contain.

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 input schema has zero parameters, so the baseline is 4. There are no parameter semantics to explain, and the description does not need to compensate for undocumented parameters because none exist.

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 identifies the tool's subject: learned weights per signal type and conversion stats derived from real outreach outcomes. It is more specific than the title, though it lacks an explicit verb such as 'get' or 'retrieve.' It does not directly contrast with sibling tools like sdr_signal_stats, but the emphasis on learned weights and conversion outcomes helps distinguish it.

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?

There is no guidance on when to use this tool versus alternatives such as sdr_signal_stats, sdr_recent_signals, or sdr_get_agent_signal_profile. The description implies it is useful for querying model-derived insight, but it does not state exclusions, prerequisites, or when another sibling would be more appropriate.

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