GPUvec

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 one useful behavioral promise: output contains no fabricated scoring or ranking. It does not cover what happens with unknown model names, duplicate entries, or lookup failures, which for a comparison tool is a real 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?

One sentence, front-loaded with the verb and resource, and every clause earns its place — the range, the distinctness rule, and the no-fabricated-ranking guarantee each add information.

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 and annotations cover the safety profile, so the description doesn't need to explain return values. Still missing: which attributes are compared, and behavior when a model is not found — details an agent needs to interpret a comparison result 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 coverage is 0%, but the description compensates well for a single-parameter tool: it says 2–4 entries, that they must be distinct, and that they must be 'exact' model identifiers — the exactness point meaningfully rules out fuzzy matching, which the schema's plain string type does not convey.

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: compare 2–4 models side by side. The negative scoping ('no invented performance score or compatibility ranking') sharpens what kind of comparison it is, but the description never names an alternative like compute_capability or get_accelerator, so sibling differentiation is left 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 2–4 distinct-model constraint implies the multi-model use case, and 'distinct' excludes duplicate entries. However there is no explicit guidance on when to pick this over compute_capability or repeated get_accelerator calls, which an agent has to infer from the name alone.

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 real domain caveats beyond that — results are source-backed, non-CUDA entries exist without a capability value, and the CC is not a toolkit/framework compatibility guarantee — but says nothing about pagination, result ordering, or what an empty result means.

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?

Two short sentences with no filler and the retrieval verb is front-loaded, so it is not bloated. But brevity here comes at the cost of substance — the second sentence is a disjoint caveat fragment, and "context" does no work.

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 values needn't be spelled out, but for a 6-filter query tool with zero parameter documentation the description should explain the filter model and pagination defaults. It explains neither, so an agent cannot confidently narrow a query without guessing.

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?

Six parameters with 0% schema description coverage, so the description must carry the load and instead names none of them. limit/offset pagination, free-text 'search', and the meaning of the status/segment/capability filters (and how they combine) are entirely undocumented; only the self-explanatory enum literals offer any foothold.

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 verb+resource is present ("Retrieve ... compute capability context"), and the domain is narrowed to NVIDIA CUDA hardware. However, "context" is vague about whether it returns a single CC value, a record, or a list, and nothing distinguishes it from sibling tools like get_accelerator or list_accelerators that plainly overlap in subject matter.

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 statement of when to call this instead of get_accelerator or list_accelerators, nor any prerequisite or exclusion. The only condition-like text ("non-CUDA is not assigned a CC") describes the data domain, not invocation guidance, leaving the agent to infer when this tool is appropriate.

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 real value beyond that by asserting determinism (same results as the site's shared engine) and declaring unit conventions, but says nothing about validation behavior, error modes, or how defaults are resolved.

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?

Two tight sentences with no filler, and the core function is front-loaded before the disclaimers. Every clause carries information an agent would otherwise have to guess.

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 values needn't be explained, and the safety picture is covered by annotations. But for a tool taking a 25-field nested input with zero field-level descriptions, the description is far too thin to let an agent populate inputs 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?

Reported schema coverage is 0% for a nested object with roughly 25 fields, many of them enums whose semantics are non-obvious (quantization vs. quantization_bits, quantization_memory auto/pinned/cached/cold, product_compression, the on_disk/mmap family). The description only clarifies the KiB-in/byte-out unit convention, leaving the bulk of the parameter space undocumented in both schema and description.

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 ("Calculate deterministic Qdrant capacity") and names the engine it uses, which is far more informative than a bare name restatement. It also draws a boundary ("not measured capacity or QPS sizing"), though it doesn't name a sibling tool to route to.

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?

"not measured capacity or QPS sizing" implies a when-not-to-use condition, which is more than most definitions offer. However, no alternative tool (e.g. compare_accelerators, list_qdrant_pricing) is named for the excluded cases, and no prerequisites are stated.

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?

With readOnlyHint already declaring a safe read, the description still adds real behavioral context: rows with unknown units/scope/count are silently excluded by numeric filters, billing matching is a case-insensitive substring, and availability is a recorded snapshot rather than live. These are non-obvious semantics an agent cannot infer from the schema or annotations.

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, with the core purpose front-loaded and each following clause carrying a distinct, non-redundant constraint. No filler and nothing repeated from the structured 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 values needn't be explained, but a 13-parameter tool with zero schema coverage needs far more parameter-level guidance than is given. Several filters (sort, paging, source_backed_only, the VRAM/price variants) remain uninterpretable without trial and error.

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 0%, so the description must carry the load, and it only clarifies billing_model matching and the strictness of numeric filters. It says nothing about sort (including the '-' prefix convention), limit/offset paging, source_backed_only's default of true, or how min_vram_total differs from min_vram_per_gpu and the two max price fields.

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 first clause gives a specific verb and resource ('Find recorded offers') plus the filtering domain ('bounded price/count/VRAM filters'), which is enough to distinguish it from list_price_history or list_providers. It stops short of naming a sibling tool or stating a negative boundary, so it lands just below a 5.

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?

'Availability is recorded, not live' implicitly steers agents away from expecting real-time data, and 'unknown units... never pass numeric filters' warns that numeric filters are strict. However, it never states when to pick this over list_price_history or per-accelerator lookups, leaving the choice to inference.

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 genuine behavior beyond that: lookups are strict and unknown models raise errors rather than returning fuzzy or hallucinated specifications, which tells the agent to avoid guessing identifiers.

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?

Two short sentences, zero filler, with the core action front-loaded and the strictness caveat immediately after. Nothing repeats the name or title.

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 one-parameter read tool with annotations covering safety and an output schema covering returns, the description supplies the essential lookup contract. The only missing piece is a concrete identifier example or a pointer to list_accelerators for discovery.

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 0% for the single `model` field, so the description carries the burden — and it does meaningfully: the value may be either a full model name or a canonical slug, and it must be exact. It stops short of giving an example slug format, which would have fully closed the gap.

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: fetch exactly one accelerator record, keyed by model name or canonical slug. The word "one" implicitly contrasts with the listing siblings, but no sibling is named, so the differentiation is only implied.

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: call this when you already have an exact identifier, since listing/discovery is elsewhere. There is no explicit "use list_accelerators to find a model" routing and no stated exclusions, so the agent must infer the alternative.

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 usefully adds the pagination cap ('At most 50 per page') and hints that results may contain unknowns/provenance gaps, but says nothing about total counts, ordering, or empty-result behavior. With annotations covering the safety profile, a 3 is appropriate.

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?

Two short sentences, front-loaded with purpose followed by the pagination constraint; no filler or repetition.

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 values needn't be explained, and pagination is acknowledged. However, for a 4-parameter filtered list tool, the absence of any guidance on the search/manufacturer filters and offset-based paging leaves real gaps for an agent trying to call it 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 0% across all 4 parameters, so the description carries the full burden — yet it says nothing about limit, offset, search, or manufacturer. The only parameter-relevant content is the 50-per-page cap, which is already the schema maximum for limit.

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 ('List recorded accelerator specifications') and adds a scope qualifier ('recorded', 'with units, field provenance and unknowns'). It is distinguishable from get_accelerator and compare_accelerators by its plural/collection nature, though it never names those siblings 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?

Browsing/listing is implied, but there is no statement of when to use this versus compare_accelerators or get_accelerator, and no mention that the search/manufacturer parameters exist for narrowing results. Usage must be inferred from the name alone.

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 real value beyond that: default sort order (newest first), that date filters apply to capture time in UTC, and a caveat that distinct configurations should not be read as a single trend. It does not mention pagination behavior or result limits, so it stays at 3 rather than higher.

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?

Two sentences, front-loaded with the core action and ordering, no filler. The second sentence is terse to the point of being slightly cryptic ('distinct configurations are not one price trend'), but it earns its place as a caveat.

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 values need no explanation, but with six parameters at 0% schema description coverage the description should explain the filter and paging parameters and does not. An agent cannot tell from this text what model/provider matching does or how limit/offset interact, which is a material gap for a filtered-list tool.

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 0%, so the description carries the full burden, yet it only clarifies the semantics of start/end (UTC capture time) and alludes vaguely to configuration via 'distinct configurations'. Four of six parameters — model, provider, limit, offset — are never explained, and the 1-50 limit cap and offset range are left entirely to the schema constraints.

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 ('Read ... GPU price observations') plus an ordering guarantee ('newest first'). The word 'approved' and 'observations' implicitly separates it from offer-listing siblings like find_offers, but no sibling is 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 only implied: the agent can infer this is the historical/observed-price read path versus live offers, but there is no explicit when-to-use, when-not-to-use, or named alternative. The second sentence hints at a misuse case (treating distinct configurations as one trend) without saying what to do instead.

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 one real trait beyond that — results are catalog/provenance data, not live pricing or stock — but says nothing about pagination behavior, freshness of the catalog, or result ordering.

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 led by the verb and resource, with no filler. Every clause carries information about scope or data provenance.

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 values need not be explained, and annotations cover safety. However, the undocumented pagination/search parameters are a genuine gap for a list tool whose result set depends on limit/offset/search semantics.

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?

Three parameters (limit, offset, search) have 0% schema description coverage, and the description never mentions limit/offset paging or what the search string matches against. With no structured documentation and no compensating prose, the agent is left guessing what 'search' filters on.

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 (List) and resource (GPU compute services/providers), then scopes it as provenance metadata distinct from priced offers and live stock. This implicitly separates it from find_offers, though no sibling is 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?

The phrase 'independent of priced offers and live stock' hints at when this tool applies (catalog/provenance lookups rather than offer/availability queries), but there is no explicit when-to-use statement or named alternative. The agent must infer the routing from context.

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?

The annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered. The description adds useful data-freshness context by saying these are 'dated' quotes rather than live prices, but it does not disclose pagination behavior or how filters affect results.

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?

The definition is a single front-loaded sentence with no wasted wording. It states the core resource and the key exclusions immediately.

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?

Although an output schema and annotations exist, the tool exposes pagination and region/provider filtering parameters that are undocumented in both the schema and the description. The description is too brief for an agent to use the optional filtering and paging capabilities 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?

There are four parameters and 0% schema description coverage, but the description never mentions limit, offset, region, or provider. An agent cannot learn from the description what the filters do, how pagination works, or what the defaults and bounds imply.

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 uses a specific verb and resource: 'List dated official managed Cloud package quotes.' It also distinguishes the resource from 'live prices' and 'hardware recommendations,' which helps separate it from sibling pricing and accelerator tools, though it does not name exact alternatives.

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?

It gives clear when-not guidance by excluding 'live prices' and 'hardware recommendations,' implying this tool is for dated official managed Cloud package quotes. It does not name specific sibling tools to use instead, so it falls short of full alternative routing.

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