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

Annotations already declare readOnlyHint=true, and the description usefully adds that the signal is aggregated sentiment rather than a price prediction, describes the 0-100 scale, and clarifies ranking and default length. This is valuable context beyond the annotations without contradicting them.

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 with no filler. The core behavior, metric meaning, default return size, and parameter tuning guidance are all front-loaded and each sentence 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?

Given the simple read-only nature, two optional parameters, and no output schema, the description covers the essential facts: output concept, ordering, default count, metric scale, and limit guidance. It could mention the window parameter explicitly, but the schema already covers it.

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 the schema already documents both limit and window fully. The description's limit guidance reinforces the schema's 'Use 1 when the user asks for the single top name' but adds little new semantic meaning.

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 states exactly what the tool does: returns companies ranked by aggregated social chatter signal strength (0-100), explicitly notes it is 'not a price prediction', and mentions the default top-5 return. This clearly differentiates it from the sibling company-specific tools like get_company_signals.

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?

The description provides practical guidance on how to adjust the limit based on the user's question ('limit 1 when asked for the number one', 'limit 10 for a longer list'). However, it gives no guidance on when to choose this tool over siblings or when not to use it, so selection between tools is only implied.

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