StackRates VPS prices

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.

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds a useful behavioral detail — answers are grounded in 'today's prices', i.e. current data — but says nothing about how answers are formed, ranking, or limits on question complexity.

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?

A single sentence with the verb and action front-loaded and the illustrative examples parenthesized at the end. No filler, and the most important information comes first.

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 read-only, one-parameter tool with no output schema, the description covers purpose, input style, and data freshness adequately. A brief hint of what an answer looks like (e.g. ranked options with prices) 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?

There is only one self-descriptive parameter ('question') and schema description coverage is 0%, but the three inline examples effectively define the expected shape of that input, showing it accepts free-form natural language with prices, brands, and workloads bundled together.

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 gives a concrete verb (answer) and resource (plain-language hosting price questions) and illustrates scope with three representative queries spanning price, comparison, and workload-fit use cases. However, it never names or contrasts itself with siblings like search_vps_plans or current_vps_offers, leaving the boundary implicit.

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?

The example questions imply when the tool is appropriate (natural-language, comparative, or needs-based pricing questions) but there is no explicit when-to-use statement, no exclusions, and no named alternative for structured plan lookups.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context beyond the annotations: the data comes from providers' own pages and is current as of today, and each offer is evaluated on its first year against the cheapest regular plan with equivalent memory, which defines what an 'offer' means here. It still says nothing about result ordering or rate limits.

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?

A single sentence with no filler, and the core concept (introductory/sale prices) is front-loaded before the evaluation methodology. It is dense but every clause carries meaning.

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 read-only, open-world=false listing tool with no output schema, the description conveys the concept of the returned data (first-year-judged offers) but omits what fields come back, ordering, and currency assumptions. Adequate but with clear gaps.

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%, so providers, min_ram_gb and max_usd_month are already fully documented in the schema. The description adds nothing about how these filters interact with the first-year comparison logic, so the baseline 3 applies.

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 states the resource (introductory/sale VPS prices scraped from providers' own pages today) but never uses a clear verb like 'list' or 'return', and offers no differentiation from siblings such as ask_vps_prices or search_vps_plans, which sound like they return overlapping data. An agent can infer it is an offers/price-listing tool, but cannot tell from the text alone how it differs from those siblings.

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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternatives. The phrase 'today' hints at freshness-driven usage, but the agent is left to guess whether this replaces or complements ask_vps_prices and search_vps_plans.

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

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

Annotations already establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds genuinely non-obvious behavior: results are cheapest-first and deduplicated to one plan per provider unless exactly one provider is given — a result-shaping rule the schema does not encode.

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?

A single dense sentence with no filler; the cheapest-first ordering — the most decision-relevant fact — is front-loaded alongside the filters.

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 an 11-parameter read-only search with full schema coverage, the description covers the essentials and notably explains the ordering/dedup behavior. With no output schema, it could say slightly more about what each result contains (price, provider, specs), but nothing critical to invocation is missing.

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 each of the 11 parameters carries its own units and examples (e.g. min_ram_gb '0.5 for 512 MB', location slugs). The description only loosely maps its four filter categories onto those parameters, adding little beyond the schema; baseline 3 applies.

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+resource (search VPS plans) and adds the matching dimensions and sort order, which is more than a restatement of the title. It does not, however, distinguish itself from siblings like ask_vps_prices or current_vps_offers, which sound like they return overlapping pricing data.

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 by the filter vocabulary (size, budget, provider, location), so an agent can infer this is the filterable lookup tool. There is no explicit statement of when to prefer it over current_vps_offers or ask_vps_prices, and no exclusions or prerequisites.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds useful scope context (192 apps, requirements plus cheapest matching plans), but says nothing about whether prices are live or cached, how 'cheapest' is determined, or how results are shaped.

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?

A single front-loaded sentence with no filler, and the app examples and 192-app count earn their place by signaling breadth. It is a noun phrase rather than a full sentence, which slightly weakens the opening.

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 must carry the return-value burden and only partially does so: it promises requirements and cheapest plans but omits whether providers, prices, or plan links are included. For a read-only lookup tool with annotations covering safety, this is adequate but incomplete.

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%, so all three parameters are already documented in the schema, and the app examples in the description largely duplicate the schema's own examples. Nothing is added about how location interacts with the cheapest-plan selection or how max_usd_month filters results, so baseline 3 applies.

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 the resource (documented requirements for a self-hosted app/game server) and the output (cheapest plans that meet them), with a concrete example list and scale (192 apps). It distinguishes itself implicitly from price-first siblings like ask_vps_prices and search_vps_plans by being app-driven, but never names or contrasts them.

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 only implied: the agent infers it should call this when it has an app in mind and wants matching hosting. There is no explicit when-to-use, when-not-to-use, or routing to alternatives such as advise_vps_choice or current_vps_offers, which is a real gap given four sibling tools in the same space.

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