results_evaluate

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

Discloses behavior for inner/outer parameters and return type ('Evaluated values as lists, or error message'). No annotations exist, so description carries the burden; however, it does not mention side effects, permissions, or any destructive potential (likely none). Adequate but not comprehensive.

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 docstring-style format (Args/Returns) is well-structured and concise. Each parameter description is a single line with examples. No unnecessary words.

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?

With no output schema and no annotations, the description adequately covers parameters but leaves the return format vague ('lists' without structure). For a tool with 6 parameters and no output schema, more detail on return values would improve completeness.

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%, but the description compensates by explaining each parameter with examples (e.g., 'es.normE', 'V/m'). This adds meaning beyond the schema's type-only definitions. Could improve by clarifying defaults more explicitly.

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 verb ('evaluate') and resource ('expression on a solution dataset'). It implies differentiation from siblings like results_global_evaluate by including a dataset parameter, but does not explicitly distinguish.

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?

No guidance on when to use this tool versus alternatives like results_global_evaluate, results_inner_values, or results_outer_values. The description only states what it does, not when to choose it.

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