get_capabilities

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

With no annotations provided, the description takes on the full burden of behavioral disclosure and does so thoroughly. It reveals a side effect ('Probes read and clear SCPI errors'), defines the evidence categories returned, and states a limitation ('measurement lists are not accuracy validation'). This goes well beyond the schema and gives the agent an accurate model of the tool's runtime effects.

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?

Three sentences deliver the core purpose, default behavior, a critical limitation, a side effect, and the parameter switch, with zero filler. The main result is stated first, and every subsequent clause earns its place by adding operational or behavioral detail.

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 tool with one optional parameter, no annotations, and no output schema, the description covers all essential context: what it returns, how evidence is categorized, what the default does, how to opt out of probing, what not to use it for, and the SCPI error side effect. Nothing an agent needs to call 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?

The input schema provides no description for verify_hardware, so the description must compensate, and it does. It explains the parameter's role by contrasting the default probing behavior with 'Set verify_hardware=false for identity/model data only,' which is directly actionable for an agent deciding how to call the tool.

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 opens with a precise verb-object pair, 'Return model capabilities with per-field evidence,' and immediately clarifies what kinds of evidence are produced: hardware-verified, documented, or unverified. It further distinguishes the tool from likely siblings like idn by grounding it in DHO800/900 channel/grid counts and model mismatches.

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 clear context about default behavior and explicitly tells the caller when to set verify_hardware=false ('for identity/model data only'). It also warns that measurement lists are not accuracy validation, which helps prevent misuse, though it does not explicitly name alternative sibling tools.

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