normalize_text

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

No annotations are provided, so the description carries the full burden. It briefly describes the effect of each profile (e.g., 'removes legal company suffixes') but does not disclose side effects, performance characteristics, or limitations. Minimal behavioral context but adequate for a simple normalization function.

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?

Very concise: two sentences plus bullet points for profiles and an Args section. No unnecessary information. Front-loaded with the purpose, making it quick to parse.

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?

The description covers purpose, usage context, parameters, and profiles. Since an output schema exists (not shown but noted), it does not need to explain return values. Lacks examples but is complete enough given the tool's simplicity and sibling context.

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%, so the description must compensate. It clearly defines both parameters: 'value' as text to normalize and 'profile' with enumerated options and their purposes. This adds significant meaning beyond the raw 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?

The description clearly states it normalizes text before fuzzy matching, and lists four specific profiles (general, company, product, address) that distinguish its functionality. The name and description together make the purpose unambiguous.

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 explicitly says 'before fuzzy matching', providing clear context for when to use this tool. While it does not explicitly exclude alternatives, the sibling tools (compare_strings, find_best_matches, etc.) are clearly about matching/comparing, so usage intent is well communicated.

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