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

Annotations already cover read-only/non-destructive/non-idempotent, but the description adds substantial context beyond them: corpus freshness and scale, ranking methodology (measured use rather than votes), a rate limit (free, twenty a day per address or account), and the deliberate absence of a match score with the reason why. For a retrieval tool this is unusually rich disclosure.

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 sentences, front-loaded with the purpose and followed by input format, output semantics, and cost. The middle sentence is long and carries a parenthetical list, but every sentence adds information and nothing is redundant.

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?

An output schema exists, so return structure needn't be spelled out, yet the description still supplies the interpretation an agent needs (up to five tools, typed requirement outcomes, no numeric score by design) plus quota and cost. Nothing material is missing for correct invocation.

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 baseline is 3; the description earns an extra point by reinforcing the free-prose nature of 'need' and enumerating the kind of constraints to embed in it (self-hosted, API, free plan, budget, who will use it). It also clarifies how returned requirement states should be interpreted, which frames what a well-formed input looks like.

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?

Specific verb (find) plus resource (AI tools) qualified by the input form (a described job) and the corpus (the INITE Atlas, ten thousand tools re-read weekly). This is clearly distinguishable from sibling retrieval tools like atlas_search or atlas_lookup, which operate on the dataset rather than matching a natural-language job description.

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 clear context for when this tool applies — you have a described job with constraints and want candidate tools — and tells the caller how much detail to provide ('a sentence or two with any constraint that matters'). It does not name an alternative or state when not to use it (e.g., versus atlas_compare or atlas_lookup), so it stops short of full routing guidance.

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