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

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), so the bar is lower, yet the description adds real value: it discloses the multi-step behavior (calls check_citations, then collects dateModified per URL), the scoring model (0-100, halflife=365d), and the bucket taxonomy (fresh/current/stale/ancient/unknown). It does not mention latency or cost of querying an AI engine, which is the main remaining gap.

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 compact sentences, front-loaded with the action and scoring outcome, followed by the workflow and the motivation for using it. No filler and no repetition of schema fields.

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-value enumeration is not required, and the description still summarizes the headline outputs (score plus per-URL buckets). Combined with the annotations covering side-effect semantics and 100% schema coverage for inputs, an agent has everything needed to invoke this correctly.

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 description coverage is 100% and the engine enum plus max_results bounds are fully documented in the schema, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already carries (e.g., no guidance on picking an engine or tuning max_results).

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?

Names a specific verb (Score) and resource (recency of pages cited for a query), and distinguishes itself from neighbors: it delegates citation fetching to check_citations and its output is a freshness score, not a citation list. An agent can separate it from citations_check, citations_trend, and citations_provenance without opening a schema.

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 a clear context for use ('Surfaces queries where AI cites old content - opportunity to ship fresher'), which tells the agent when this tool is the right pick. It does not name alternative tools or state exclusions, so it stops short of explicit when-not guidance.

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