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

With no annotations available, the description carries the full behavioral burden and does it well. It discloses that API prices come from a dated snapshot, that the result reports its age and flags staleness, and instructs the agent not to present old prices as current. This is a meaningful, non-obvious behavioral 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, each earning its place: the first explains what the tool computes, the second gives real user intents, and the third warns about stale pricing. The outcome is well structured and front-loaded.

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?

The description is complete on purpose, behavior, and the staleness caveat, but it does not cover the 12-parameter input surface or any expected return format beyond the three high-level outputs. For a tool with no output schema and no annotations, that leaves a meaningful completeness 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?

Schema description coverage is 0% and there are 12 parameters, so the description needed to compensate. It only implies concepts like workload and traffic without explaining properties such as hardware, model, duty_cycle, latency_slo_ms, quality_target, or avg_output_tokens. An agent would struggle to set these correctly based solely on this 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?

The description names a specific action (compare), a specific subject (self-hosting vs. hosted APIs), and the three concrete outputs: cheapest feasible GPU fleet sizing, price comparison for identical traffic, and break-even token volume. This clearly differentiates it from siblings like 'plan' and 'suggest'.

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 description gives explicit, user-quoted use cases: 'is it cheaper to self-host or use the API' and 'when does a GPU pay for itself'. It does not explicitly name alternatives or say when to use a sibling tool instead, so it stops slightly short of a 5.

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?

No annotations are provided, so the description carries the full burden. It does not disclose any behavioral traits such as output format, size limits, or whether it reflects real-time data. The simple listing implies read-only behavior but this is not explicitly stated.

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 description is a single, focused sentence that directly states the core action and output characteristics. There is no wasted text, and it is immediately understandable.

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 simple, parameterless listing tool with no output schema, the description is adequately complete. It identifies the resource (GPUs) and the key attributes (VRAM/bandwidth/TDP/interconnect). However, it could marginally benefit from a note on data recency or ordering, but this is not critical given the tool's simplicity.

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?

The tool has zero parameters, so the baseline score is 4. The description correctly implies no input is needed. Since there are no parameters, no additional semantic information is required.

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 clearly states the tool's function: 'List known GPUs' with specific attributes (VRAM/bandwidth/TDP/interconnect). This is a specific verb+resource and clearly distinguishes from siblings like chimeraforge_plan and chimeraforge_resolve_model.

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 guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions. The description is purely functional with no usage direction.

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 no annotations, the description carries the full burden, and it does disclose two genuine behavioral traits: results come back labeled with per-number provenance (measured/extrapolated/estimated/unknown), which tells the agent how much to trust each figure, and engine availability is gated by the platform's own documentation. It stops short of stating the obvious-but-unconfirmed fact that this is a pure analysis call with no side effects, and says nothing about cost, latency, or what happens when nothing fits beyond a short reason.

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 tight sentences with the core capability front-loaded, followed by return-shape and use-case routing, then a parameter caveat. No filler or repetition. The platform sentence is somewhat tacked on at the end and could be grouped with the framing, but nothing is wasteful.

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 40-parameter tool with no annotations and no output schema, the description should be doing far more work than it is. It usefully characterizes the return value via provenance labels even without an output schema, but it leaves the semantics of nearly every input undocumented, gives no prerequisite or scoping guidance, and never states the invocation cost of such a large search space.

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 40 parameters at 0% schema description coverage, and the description only gives meaning to one of them (platform). High-impact inputs such as gpu_overrides, allow_offload, use_contributions, quality_target, gpu_price_multiplier, prefix_cache_hit_rate, duty_cycle, safety_target, and the parallel/SLO knobs are left completely opaque, so an agent has little basis for filling them meaningfully rather than relying on defaults.

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 sentence names a specific verb (recommend) and a well-defined resource (model x quantization x backend x GPU-count deployment), and even states the failure mode ('or report why nothing fits') plus the return shape (candidates with provenance). It is very clear what the tool does, but it never names or contrasts with the ambiguous sibling chimeraforge_suggest, leaving the agent to infer the boundary from context alone.

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 'Use for:' list gives four concrete query shapes ('what GPU do I need for <model>', 'will <model> fit on <gpu>', 'how many GPUs for N req/s', 'what will it cost'), which is strong intent-routing guidance. It also gives an actionable platform rule (set platform to the deployment OS; engines are offered only where their docs say they run). What is missing is any explicit when-not-to-use or named alternative, so the agent must guess whether a 'what GPU do I need' question belongs here or in chimeraforge_suggest.

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?

No annotations are provided, so the description carries the full burden. It hints that results are grounded/real rather than generated, but omits critical behavior: that allow_network defaults to true (this call may hit the network), how hf_revision affects resolution, what happens on failure, and whether results are cached.

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; the core action and purpose come first. The parenthetical is slightly jargon-y but earns its place by conveying the anti-hallucination intent.

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 annotations and no output schema, the description should explain more about the return shape ('params/architecture' is only a gesture) and the network/revision behavior of the tool. Adequate as a minimum but leaves meaningful gaps for a 3-param resolution 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% across 3 parameters, so the description must compensate. It only loosely implies what 'model' means (a model id) and says nothing about hf_revision or allow_network, leaving two of three parameters 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?

Specific verb+resource: 'Resolve a model id to real params/architecture' clearly states the transformation performed. The parenthetical '(grounds hallucinated specs)' reinforces intent. It is distinguishable from siblings like list_hardware or compare_api, though it never names 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?

The parenthetical implies the use case (verify model specs rather than trust hallucinated ones), which is a soft usage signal. However, it gives no when-not guidance, no prerequisites, and never points to an alternative among the siblings.

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?

No annotations exist, so the description carries the behavioral disclosure burden. It communicates core behavior: ranking models against a fit/SLO objective and drawing from three sources: catalog, ollama, and hf. However, it does not disclose external interactions (e.g., reaching out to Ollama or Hugging Face), side effects, failure modes, or whether the operation is read-only.

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 carry the core behavior, user-facing examples, and source list without redundancy. The most important action is front-loaded: 'Rank the models...' instead of burying the intent in filler.

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?

The description covers core purpose and high-level sources, which is enough to make a reasonable first call with the one required parameter. However, with 11 parameters, no output schema, no annotation safety clues, and 0% parameter documentation coverage, the description omits too much detail to fully steer an agent through meaningful variations of source, SLO, latency, and quality trade-offs.

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 compensate, but it only adds meaning to the source concept ('catalog', 'ollama', 'hf') and general context around SLO. The other 10 parameters, including latency_slo_ms, request_rate, quality_target, hf_limit, and budget_usd_month, are left to the agent to infer from their names and defaults, which is risky for a tool with this much configuration surface.

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 object: 'Rank the models that actually fit and hit the SLO on a given GPU.' It clearly identifies itself as the inverse of planning, which separates it from its most likely sibling confusion. The example use cases ('what can I run on a 4090', 'best model for 12GB') make the intent immediately understandable.

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 description provides concrete usage framing with quoted examples and explicitly contrasts it with planning. It would be stronger if it also stated when not to use it or pointed to the sibling tools, but the 'inverse of planning' hint gives the agent enough context to route appropriately.

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