GLM-Aware Interpretation

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

No annotations are supplied, so the description carries the full behavioral burden, and it leaves key traits unstated: whether the call is read-only, whether it contacts a GLM device/API, whether a GLM version mismatch causes errors, and what the recommendations look like. 'Calibration-aware recommendations' and the explain-fix split give some sense of the output, but the mutation/side-effect profile is entirely undocumented for a zero-annotation tool.

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 tight sentences that front-load the purpose and then the output shape (what GLM can address, what needs physical solutions, recommendations). No filler or repetition, though 'measurement analysis results' could be sharpened to name the specific upstream artifact.

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 multi-parameter, advisory tool with no annotations and no output schema, the description is too thin. It does not define what analysis_results should contain, how comparison_id differs from measurement_id, or how GLM version changes the advice, leaving an agent unable to construct a correct call from the definition alone.

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 0% and the description never mentions any of the four parameters. The crucial question of whether measurement_id, comparison_id, and analysis_results are alternatives or must be combined is left entirely unaddressed. Only glm_version's enum values are self-explanatory, which keeps this above a 1.

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 names a specific verb and resource: interpret measurement analysis results, scoped to Genelec GLM's capabilities and limitations. That distinguishes it from the analyze_* and optimize_room siblings, which produce analysis rather than interpret it against hardware constraints. It stops short of naming which sibling's output it consumes, so it is clear but not perfectly differentiated.

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?

Usage is implied rather than stated: the tool is for the post-analysis step of deciding what GLM can fix versus what needs physical treatment. There is no explicit when-to-use, when-not-to-use, or reference to alternatives such as rew_optimize_room or rew_compare_to_target. An agent must infer the sequencing from the phrase 'measurement analysis results'.

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