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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description needn't rehash safety. It adds value by enumerating the elements stripped (headers, bold, italic, code fences, links, lists) and explaining the cost-reduction benefit, which goes beyond the annotations.

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?

Two tight sentences: first states the action and output, second gives the usage context and rationale. Every word earns its place; zero fluff.

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, read-only, idempotent tool with an output schema, this description covers purpose, usage, and behavior sufficiently. There are no unresolved questions about invocation or results.

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 input schema already describes the sole parameter as 'Markdown text to convert to plain text' with 100% coverage. The description repeats this concept without adding syntax or format details, so it meets the baseline but doesn't exceed it.

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 action ('Strip all Markdown formatting') and the expected output ('return clean plain text'), with explicit examples of formatting elements. This makes the tool's purpose unmistakable and distinguishes it from conversion tools like html_to_markdown.

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?

It explicitly instructs when to use: 'Run this before injecting scraped documentation, README files, or user content into an LLM prompt.' This provides clear context and a concrete use case, though it doesn't contrast with alternatives.

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