feature_search

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

With no annotations provided, the description does not disclose behavioral details beyond the search mechanism. It mentions 'surface the matching entry near the top' suggesting relevance ordering, but does not address pagination, result format, error handling, or rate limits. The description adds some context but not comprehensive behavioral transparency.

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 a single paragraph that packs in many examples and content types. While informative, it could be more concise by grouping examples or using bullet points. It front-loads the purpose well but loses some conciseness due to excessive examples.

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 relative simplicity (two parameters, no output schema), the description covers the input semantics and search scope adequately. It explains what content is searched and provides query patterns. With no output schema, it could hint at return structure, but the examples suffice for an agent to understand expected behavior.

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 min/max lengths and range for limit, but the description enriches this by listing example queries and clarifying the scope of searchable content. It goes beyond the schema's parameter descriptions, which are somewhat generic, by showing realistic usage patterns.

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 explicitly states the tool performs free-text search across a specific catalog of marketing features, plans, integrations, etc. It lists diverse example queries like 'vs Xero' and 'GSTR-3B due', making the purpose unmistakable and distinguishing it from more specific sibling tools like list_features or list_integrations.

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 implies usage for open-ended queries across many content types, contrasting with sibling tools that are lists or analyses. It provides concrete query examples that guide the user, but lacks explicit 'use when' or 'use instead of' statements. The examples implicitly show when to use this tool over others (e.g., searching for competitive information vs list_competitors).

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