normalize_phonetic_text

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does disclose the algorithm and the 0.005 USDC cost, which is useful, but it does not describe the return format, whether state is modified, or how payment is enforced. For a pure compute tool this is a moderate gap.

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 entire description is one efficient sentence that front-loads the action and algorithm, then gives concrete use cases, with cost appended in parentheses. There is no filler or redundancy.

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?

This is a simple stateless compute tool, and the description covers purpose, algorithm, and cost. However, there is no output schema and the description does not explain what the returned hashes look like or how to construct the payload/payment fields, leaving some uncertainty for 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?

Schema description coverage is 100%, so the baseline is 3. The schema descriptions are generic wrappers (payload, paymentSignature), and the description adds domain context about phonetic hashing but does not specify the expected payload structure, such as whether the input should be a raw string or a JSON object with specific fields.

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 uses a specific verb ('Computes') and names specific algorithms ('Double Metaphone and Soundex phonetic hashes'), with clear use cases: misspelled names, speech-to-text errors, and slang. This distinguishes it cleanly from text-normalization and validation siblings like normalize_unicode_nfc or validate_code_syntax.

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 gives clear context for when to use the tool: phonetic fuzzy matching for names, transcripts, and slang. It does not name alternative tools or state when not to use it, so it stops short of full when/when-not guidance.

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