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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds context annotations cannot: the backing dataset (endoflife.date product list, ~490 products), fuzzy matching across names/labels/aliases, best-match ordering, and a per-call cost of 1 credit. Missing is any statement about result shape beyond ordering, but the added behavioral value is real.

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?

Front-loaded with the core purpose and aliased examples, then sequencing guidance, then source/cost metadata at the end where it belongs. Every sentence carries information, though the parenthetical cost note and the dataset-size aside make it slightly denser than strictly necessary.

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?

With no output schema, the description carries the return-value burden and does reasonably: it says the tool returns slugs, ordered best-first, and returns nothing for untracked products. Combined with the cost note and usage ordering, an agent has enough to call it correctly. A brief note on what a match object contains beyond the slug would close the remaining gap.

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 the mechanical fields are documented already. The description still adds meaning: the query is an everyday name or alias rather than an exact canonical name, matching spans names, labels and aliases, and results are ranked. That directly informs how to phrase a query and how to interpret a near-miss. The limit parameter is left entirely to 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?

States a specific verb and resource (find the endoflife.date product slug) and explicitly covers the secondary purpose (checking whether a product is tracked at all). Concrete examples ("postgres", "k8s", "node", "windows server") make it instantly distinguishable from siblings like eol_latest_release or eol_products_by_category, which operate on a 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?

Gives an explicit sequencing rule: "Use it before the other end-of-life tools." It also supplies the negative case — untracked software returns no match — which tells the agent when to stop. It does not name a specific alternative for the case where the agent already knows the slug, so it falls short of a full when/when-not/alternative triad.

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