categorize_by_memo

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

No annotations are provided, so the description must carry the burden. It explains the matching logic and fallback to 'Uncategorized', and that it returns tagged rows with counts. However, it does not explicitly state that the tool is read-only or if it modifies the original file. It also doesn't mention error handling or required permissions.

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 three sentences, each serving a purpose: the first states the core function, the second gives domain context, and the third explains the rules and output. No wasted words, and the most critical information is front-loaded.

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 that an output schema exists (as per context), the description is complete enough. It covers the input parameters, matching behavior, and output shape (tagged rows + per-category breakdown). It assumes reasonable domain knowledge about exports and rows but provides sufficient detail for correct invocation.

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 has 0% description coverage, but the description compensates by explaining the roles of all three parameters: file_path (via export context), memo_columns (pass both main and line), and rules (maps category to keyword list with first-match wins). This adds significant meaning beyond the bare schema, though it lacks examples or constraints.

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: tagging rows with a category based on case-insensitive substring matches across memo columns. It specifies the verb (tag), resource (rows from a NetSuite GL export), and method (keyword matching). The purpose is distinct from sibling tools like aggregate_export or detect_anomalies.

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 context for when to use the tool, e.g., for NetSuite GL exports with memo columns. It implies usage for categorization tasks but does not explicitly compare to siblings or state when not to use it. The guidance is clear enough for an AI agent to infer appropriate use.

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