analyze_response

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

With no annotations, the description carries the full burden. It discloses what the tool computes and outputs, implying read-only behavior, but doesn't explicitly state safety, error conditions, or side effects. The lack of explicit non-destructive confirmation is a gap, though the analysis nature mitigates concern.

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, front-loaded with the core action and outputs, followed by a clear usage statement. No redundant or filler content.

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 tool's moderate complexity (3 params, no output schema, no annotations), the description provides sufficient context: what it does, what it returns, and when to use it. It could mention prerequisites or output format, but the listed outputs and clear purpose make it reasonably complete.

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 parameters are already documented. The description adds no extra parameter nuance beyond what the schema provides. Baseline of 3 is appropriate as the schema does the heavy lifting.

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 the tool interprets a measurement's frequency response, listing specific outputs (per-band levels, flatness, peaks/nulls with frequency, deviation, Q, severity). It distinguishes itself from siblings like get_frequency_response by positioning as 'the primary tool for answering what is wrong with this response?'

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 explicitly says it's the primary tool for diagnosing response issues, giving clear usage context. It doesn't name specific alternatives or when not to use it, but the positioning implies when to prefer it over raw data retrieval tools.

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