Search nets

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

Goes well beyond the readOnly/idempotent annotations: discloses case-insensitivity, result ordering and keying, the notes field on zero matches, whitespace-trimmed matching, and that match-all patterns are rejected. These are exactly the behaviors an agent needs to predict outcomes.

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?

Front-loaded with the purpose, then progressively deeper caveats. Slightly long, but every sentence (prefixing, trimming, match-all rejection) conveys a distinct behavior rather than restating the schema.

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?

No output schema exists, and the description compensates by describing the return shape (sorted, keyed by design name, notes on empty). Combined with full schema coverage on the three parameters, an agent has what it needs to call it correctly.

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 coverage is 100%, so the baseline is 3, but the description adds real semantics for the pattern parameter: case-insensitivity, trim behavior, and the hierarchical sheet-path prefixing that changes how patterns must be written. The design and design_variant parameters are left to the schema.

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?

States a specific verb and resource ('Search for nets matching a regex pattern') and immediately establishes the matching model. It is clearly distinguishable from list_nets, which is named as the unfiltered counterpart.

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?

Explicitly routes the agent: use list_nets for full results when a pattern would match everything, prefer unanchored patterns over anchored ones for hierarchical nets. It gives both the when and the when-not with concrete examples.

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