Validate a portable electronics experiment

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

The description adds meaningful behavioral detail beyond the annotations: it specifies that invalid input returns 'ok=true and data.valid=false with diagnostics.' This discloses a key aspect of the tool's behavior (validation outcome for invalid input). Annotations already signal read-only, idempotent, and closed-world behavior, and the description does not contradict them. Slightly more context on valid output could be included, but the provided detail is useful.

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 concise, with two sentences that front-load the core purpose and then mention the invalid-input behavior. No wasted words or redundancy, and the structure is clear: function, scope, and outcome for a key case.

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 complexity (validation against multiple contract facets), the description captures the essential purpose and a notable edge case. Since an output schema exists, it can document return values, and the description does not need to explain them. The lack of context about the exact contract format is a minor gap, but the description is adequate for a validation tool with moderate complexity.

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 schema description coverage is 0%, so the description must compensate. However, it only says 'arbitrary JSON value' and does not elaborate on the expected structure of the 'experiment' parameter. The contract types (circuit, firmware, probe, assertion, timing) are mentioned but not connected to how the JSON should be organized. The schema itself only defines a generic JSON type, so the description adds little practical meaning for constructing a valid input.

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's purpose: it validates an arbitrary JSON value against a 'simulator-neutral circuit, firmware, probe, assertion, and timing contract.' The verb 'Checks' is specific, the resource is 'experiment,' and the scope (contract validation) distinguishes it from sibling tools focused on design inspection or analysis.

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 provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It does not mention that this is for experiments specifically, nor does it reference sibling tools like crumb_validate_design. The agent must infer usage solely from the tool name and the phrase 'portable electronics experiment,' which is minimal guidance.

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