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

The readOnlyHint annotation already tells the agent this is a safe read operation, so the description does not need to restate that. The description adds a practical interpretation of the data (rising negative share warrants investigation) but does not disclose additional behavioral details such as counting semantics, aggregation windows, or zero-data behavior. This is acceptable given the annotation coverage.

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 two sentences with no waste. It front-loads the core output, then the optional parameter, then the practical use guidance. Every 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?

For a simple read-only analytics tool with only two fully documented optional parameters and an output schema, the description is complete. It tells the agent what the tool returns, how to optionally narrow it, and how to interpret the result. No critical information needed to invoke it correctly is missing.

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 input schema fully documents both time_filter and answer_types. The description only lightly reinforces the answer_types parameter with "Optionally restrict to specific answer types," adding no meaning beyond the schema. Baseline 3 is appropriate.

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 what the tool returns — counts of 'positive', 'negative', and 'neutral' sentiment for chat answers — and mentions optional restriction by answer type. It does not explicitly differentiate itself from the similarly named sibling get_answer_type_distribution, so it stops short of a perfect score.

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 gives a clear use context: "Use this as a quick quality signal." It also suggests a follow-up action when negative sentiment rises (drilling into read_sessions), which orients the agent toward a concrete workflow. It does not explicitly state when to prefer this over get_answer_type_distribution or other siblings, but the context is sufficiently clear.

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