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 fully covered. The description adds dataset scale (700+ products) and the fact that results are slugs, but nothing about matching behavior beyond the schema's case-insensitive substring note.

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?

Three short sentences, front-loaded with what the tool does, then the usage directive, then the return type. No filler and no redundancy with the schema.

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 one-parameter lookup tool with an output schema, full annotation coverage, and 100% schema description coverage, the description supplies everything an agent needs: scale, lookup role in the workflow, and the canonicalization example.

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 coverage is 100%, so baseline is 3, but the description adds real semantic value by showing the mapping example "postgres" → "postgresql", which clarifies that the query is a loose substring and the output is a canonicalized slug rather than a literal match.

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?

States a specific verb and resource (list/search products) with scope (the 700+ products endoflife.ai tracks) and the underlying intent (resolving a canonical slug). It is clearly distinguishable from siblings like get_product_lifecycle or check_eol, which consume the slug this tool produces.

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?

Explicitly says to call this before the other tools to obtain the canonical slug, which is strong sequencing guidance. It stops short of naming a specific alternative or stating when not to use it, but the workflow context is unambiguous.

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