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

The description adds operational detail beyond the readOnlyHint=true annotation: it notes that no API key is needed and describes the return payload (full haystack, question, metadata). It also clarifies the generation behavior (embedding a target fact at a specified position). No contradictions with 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?

Four short sentences cover purpose, behavior, use case, and operational requirements. No filler or redundant phrases.

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 what the tool does, when to use it, what it returns, and auth requirements. With an output schema and full parameter schema present, there are no significant gaps in 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?

The input schema already provides full descriptions for all four parameters (100% coverage), including defaults and enum options. The description adds little parameter-specific detail beyond implying position and token size, so baseline 3 is appropriate.

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 tool generates a needle-in-a-haystack test by embedding a target fact into filler text, and explicitly identifies the purpose (testing LLM context window retrieval). This distinguishes it from sibling text-generation tools like lorem_ipsum or few_shot_formatter.

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 recommends the tool for testing LLM context window retrieval accuracy, giving a clear context. It does not mention exclusions or alternative tools, so it stops short of full usage guidance.

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