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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral detail beyond that: the query is an optional case-insensitive substring filter, and the tool returns matching product slugs. No contradiction 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?

Two sentences deliver the purpose, the optional-query behavior, the return value, and an illustrative example with zero wasted words. The main function is front-loaded and the example is compact yet clear.

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 is complete for this simple 1-optional-parameter tool. It states what the tool does, how the query works, what it returns, and why to use it before other tools. The output schema covers return values, and annotations cover safety, so nothing crucial is missing.

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 query parameter as an 'Optional case-insensitive substring filter,' so schema coverage is 100%. The description enriches this with a concrete example ('postgres' → 'postgresql') and explicitly ties the parameter to finding a canonical slug, adding meaning beyond the schema.

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 a specific verb and resource: 'List or search the products endoflife.ai tracks (500+).' It also explains the tool's role as the canonical-slug lookup for other tools ('before calling the other tools'), which distinguishes it from the sibling tools that perform EOL, SBOM, and risk checks.

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 phrase 'before calling the other tools' explicitly tells when to use this tool, and the example 'postgres' → 'postgresql' illustrates the intended use case. It does not list exclusions or alternative tools, but the context is clear enough for an agent to know this is the prerequisite slug-resolution step.

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