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

No annotations are provided, so the description carries the full behavioral burden. It does not disclose what 'noise' is stripped, what normalization rules apply, whether the operation is idempotent, or what the output looks like.

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?

Single short sentence, front-loaded with the verb and resource, no filler. It is efficient, though the brevity comes at the cost of the missing detail scored elsewhere.

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?

With no annotations, no output schema, undefined transformation semantics, and two ambiguous input fields, the definition leaves too much for the agent to infer before calling 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 description coverage is 100%, so the baseline is 3. The description adds nothing about parameters, and it does not resolve the ambiguity created by two near-duplicate fields ('text' and 'input') that the schema describes only as 'Text to process' and 'Input to process'.

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 states a verb and resource ('Clean text') and adds a rough scope ('strip noise and normalize'), so the general intent is inferable. However, 'noise' and 'normalize' are unspecified against a sibling set full of overlapping string utilities (x402-normalize-whitespace, x402-remove-non-ascii, x402-remove-punct, x402-strip-html), and nothing distinguishes this tool from them.

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?

No guidance on when to use this versus the many single-purpose normalizers in the sibling list, and no prerequisites or exclusions. The agent must guess whether this is a superset or a specific pipeline.

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