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

With no annotations, the description must carry behavioral burden; it usefully discloses 'Auth mode: paid' and the exact price ($0.003/call via x402/MPP), which an agent genuinely needs. However it says nothing about the output/return behavior of the transform, so the disclosure is partial.

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 text is a metadata dump (origin URL, hostname, brand, family key, endpoint, repeated keywords) with the actual purpose appearing late as 'Summary'. The summary is duplicated verbatim as 'Source description', and keywords/use cases are itemized redundantly.

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?

For a single-parameter text transform with no output schema, the description covers purpose, triggering scenarios, auth, and cost, which is most of what an agent needs. It omits the return value and any parameter format detail, leaving gaps.

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?

There is 1 required parameter ('text') at 0% schema description coverage, so the description must compensate. It only implies the input is a Persian/Arabic string mixing scripts, giving no format, length, or encoding guidance for the parameter.

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 embedded summary states a specific verb and resource: 'Fix bidirectional text rendering in Persian/Arabic strings by isolating LTR runs.' This is distinct from siblings like persian_normalize or persian_digits, though the meaning is buried under origin/endpoint metadata rather than stated up front.

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?

Three explicit 'Use case' lines give concrete scenarios (mixed English/Persian strings, numbers/Latin appearing out of order, repairing text before display), which is strong when-to-use context. No when-not or named alternatives are given.

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