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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds real behavioral context beyond them: all numbers come from the dataset, the first line discloses whether a small language model or a rule parser interpreted the question, and the filter is described as editable. It still says nothing about latency, cost, or how it behaves on an unanswerable question beyond 'what the data cannot answer'.

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 a concrete buyer example and then the ordered contents of the reply, so the agent learns the purpose before the mechanics. It is one dense run-on paragraph rather than a list, but nearly every clause carries distinct 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?

There is no output schema, so the description carries the return-value burden and does so well: it names the read-back filter, the matching plans with term cost, the two-to-four sentence trade-off text, the data-limitation note, and the model-versus-parser provenance line. Minor gaps remain around result ordering, empty-match behavior, and what the model/parser line means in practice.

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 reinforces the filter's conversational role ('pass the previous answer's filter to continue') and implies term-based costing, but adds no syntax or format detail for limit, country, or term_months beyond what the schema already documents with its own descriptions and enum.

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 names a specific capability (answering a free-text VPS buying question from current prices) and enumerates the shape of the answer: read-back filter, matching plans with term cost, a short trade-off paragraph, and known data gaps. This clearly separates it from search_vps_plans and ask_vps_prices, which surface raw listings/prices rather than a synthesized recommendation, though those siblings are never named explicitly.

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?

Usage is implied rather than stated: the 'messy buying question in plain words' framing tells the agent this is the natural-language entry point, and 'Pass the previous answer's filter to continue a conversation' gives a multi-turn rule. But there is no explicit when-to-use-this-vs-alternative guidance (e.g. when to prefer ask_vps_prices or search_vps_plans), and no stated exclusions.

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