InferIndex

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

With annotations already declaring readOnly/idempotent/non-destructive, the description adds real semantic context: default exclusion of stale prices and flex/batch tiers, and the important interpretation rule that an absent condition means 'not published', never 'no', and that unlisted flags are false. It does not discuss rate limits or fresh-vs-cached data timeliness.

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 the core purpose and price unit, then filters, then return shape. Sentences are dense but every clause carries information; the interpretation rule about absent conditions is worth its length.

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 16-parameter tool with no output schema and no destructive semantics, the description covers purpose, default behavior, filters, return shape, and interpretation of missing data. A brief pointer to which sibling to use when would make it fully complete.

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 baseline is 3; the description adds meaning beyond the schema by explaining the default behavior of detail (compact winner+short lines), default limit (5), and the strict/absent-condition interaction. This helps the agent interpret filters correctly rather than just pass them.

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+scope: current cheapest API offers for one model across direct providers and aggregators, with the price unit (USD per 1M tokens, input/output/blended 3:1). This clearly separates it from siblings like compare_providers and price_history.

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: it explains default exclusions (stale prices, flex/batch tiers) and that optional filters can narrow results, but never says when to prefer this over compare_providers or estimate_cost. The one routing hint (use search_models when unsure) lives in the schema, not the description.

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 cover the read-only/idempotent/non-destructive profile, and the description adds real behavioral value on top: default result count, cheapest-first ordering, the default-vs-full verbosity tradeoff, and that providers lacking published info are kept and flagged unless `strict` is set. It does not describe pagination or how missing data appears in output, but the annotation bar is met.

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 behavior (what a row is, ordering, default size, winner-in-detail). Both sentences carry information, though the second is long and slightly run-on with its list of caveats.

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 13-parameter comparison tool with no output schema, the description covers the result shape and the compact/full distinction well enough to call it correctly. Remaining gaps (pagination, precise flagging format) are minor given the annotations and high schema coverage.

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 the schema already documents all 13 parameters, including the two enums. The description restates the `detail` semantics (context, published conditions, status-page reliability) but adds little that the schema does not already carry, 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 names the resource precisely (current offers for one model, one line per provider and source) and the ordering (cheapest first, 10 by default), which lets an agent distinguish it from `cheapest` and `estimate_cost`. It stops short of explicitly contrasting itself with those siblings, so a 4 rather than 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?

It gives a clear when-to-use for the `detail: "full"` option, but never says when to reach for this tool over `cheapest`, `estimate_cost` or `price_history`, nor does it state prerequisites for the comparison. Usage is implied rather than routed.

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 readOnly/idempotent/non-destructive, so safety is covered. The description adds real behavioral context the annotations don't: tiered pricing and prompt-cache pricing are factored in, offers are sorted cheapest-first, and the return layout (one detailed winner plus short lines) is disclosed.

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 purpose and then the output structure, with no filler. It is dense but every clause carries information; slightly heavy sentence construction keeps it from a 5.

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 usefully compensates by describing the returned offer list and its ordering. For a 7-parameter tool it leaves the alternative-selection question unanswered, but nothing needed to call it correctly 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 coverage is 100%, so baseline is 3, but the description adds meaning beyond the schema by tying requests_per_day to a monthly estimate and explaining that the prompt-cache price feeds the cached_ratio calculation. It does not, however, supply defaults or formats for prompt_tokens/output_tokens.

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 specific verb+resource ('estimated cost of a workload on one model at each provider') and even characterizes the output shape, so the agent knows exactly what it computes. It does not, however, contrast itself with the sibling tools (cheapest, compare_providers), so it falls short of 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?

Usage is only implied: the conditional 'per month if requests_per_day is given' hints at when the monthly figure appears, but there is no explicit statement of when to pick this over compare_providers or cheapest, nor any prerequisite guidance. Adequate but with a clear gap.

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 read-only, idempotent and non-destructive, so safety is covered. The description goes beyond them by defining each tier's meaning (guaranteed vs community vs spot), noting default volume (3 cheapest per tier, up to 50 for full), and stating prices are provider-published and dated with non-comparable normalizations.

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 what is returned before the tier and comparability caveats, and no sentence is pure filler. The telegraphic style is dense but compact enough for the amount of semantics it conveys.

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 carries the return-shape burden and does so: one block per tier, each with its own cheapest offer and per-configuration breakdown. Combined with tier definitions and the price caveat, an agent has enough to call and interpret the tool 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 100%, so the baseline is 3. The description adds real meaning beyond the schema by explaining tier semantics and the cheapest_by_gpu_count normalization rule (single-GPU vs multi-GPU node prices are not comparable), which affects how the agent should interpret 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?

States a specific resource and scope: rental offers for one GPU type, broken out by tier, with the fields returned named (provider, price, GPU count, billing, region). It is clear what the tool returns, but it does not explicitly differentiate itself from the sibling 'cheapest', which sounds like it also surfaces low-price offers.

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 routes the agent to list_gpus for the required GPU id and warns that tiers and per-configuration prices are never comparable, which is useful usage context. However, it never says when to prefer this tool over 'cheapest', 'compare_providers' or 'estimate_cost', so alternative selection is left 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 readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds real semantic context beyond that: prices are per GPU per hour as published by each provider and dated, and the explicit rule that tiers are different products and are never compared with each other — a caveat that prevents incorrect cross-tier aggregation.

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 what is tracked and then the per-tier pricing rule. Dense but every clause carries meaning; the parenthetical examples (H100, A100, L40S) and tier list are useful rather than padding.

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 describe the return payload, and it does so precisely: one row per tracked GPU type with lowest price per GPU/hour, tier, and GPU count. It omits ordering and any notion of how many entries come back, but for a small enumerated list this is close to complete.

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 takes no parameters, so the schema cannot carry semantic load and the baseline for a zero-parameter tool applies. The description appropriately spends its words on what is returned rather than on nonexistent inputs.

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 resource (GPU types tracked by InferIndex) and enumerates the returned fields: lowest price per GPU/hour in USD per tier, tier names, and GPU counts. This is far more specific than a tautology, though it never names a sibling tool to distinguish itself from 'cheapest' or 'gpu_rentals'.

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?

No when-to-use guidance and no alternatives named, despite siblings like 'cheapest', 'gpu_rentals', and 'compare_providers' that plausibly overlap. The only implicit cue is that this lists tracked GPU types rather than answering a pricing question, which the agent must infer.

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 and idempotentHint, so safety is covered. The description adds behavioral detail beyond that by specifying the data scope: 'every offer tracked by InferIndex', granularity options (daily/weekly min/max/last price, raw price changes), and the inclusion of the lab's official price over time.

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 with no filler. The first sentence front-loads the resource ('one model') and the return content; the second gives a compact usage rule for date selection. Every clause earns its place.

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 needs to cover return content, which it does by listing USD min/max/last prices, raw changes, and official lab price. Missing details like ordering or pagination are minor and not essential for correct invocation.

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 baseline is 3. The description adds value by clarifying that the date parameters are alternatives ('Give either days, or from/to, or at'), which is not made explicit in the individual parameter descriptions. It does not need to repeat schema details, so 4 is appropriate.

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 explicitly states the tool returns 'Price history of one model', making the resource and scope clear. It contrasts with sibling tools like cheapest and compare_providers by focusing on historical tracking rather than comparisons or estimates, so an agent can distinguish it without opening schemas.

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 explains how to specify date windows ('Give either days, or from/to, or at'), which is parameter-level guidance. However, it does not explicitly state when to prefer this tool over search_models, cheapest, or compare_providers; the 'one model' phrase provides only weak inference rather than clear when-to-use guidance.

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 readOnly, idempotent, and non-destructive. The description adds valuable context: it returns matching IDs and names, orders by best match first, and supports partial names. This goes beyond what annotations provide.

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 concise sentences with the primary purpose front-loaded and no filler. Examples are embedded naturally.

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?

Since there is no output schema, the description appropriately states what is returned (IDs and names, best match first). It omits edge-case handling (e.g., no results) but is sufficient for a typical lookup scenario.

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% for the single query parameter. The description adds examples and clarifies partial matching, which is helpful but only a modest enhancement over the schema's 'Model name or part of it'.

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 ('Find') and names the resource ('exact id of an LLM tracked by InferIndex'), with concrete examples. It clearly distinguishes this lookup tool from cost-focused siblings like cheapest and estimate_cost.

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 for retrieving model IDs from names, but there are no explicit alternatives or when-not-to-use conditions. The tool's purpose makes the context clear, but it does not state exclusions or trade-offs versus sibling tools.

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 only cover the read-only/idempotent safety profile, and the description adds substantial context beyond that: measured vs derived vs lower_bound throughput semantics (including the 2,048-token floor for lower_bound), refusal behavior and its causes, the settled_alternative field being for comparison not recommendation, and an explicit list of what costs are excluded (engineering, monitoring, redundancy, storage/network).

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 the core question and the headline verdict, and every clause carries information. It is dense with nested parentheses in a single block, which hurts scanability, but there is little pure 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?

For a complex 13-parameter analytical tool with no output schema, the description fully specifies the return payload (headline, headline_kind/values, break-even volume, required throughput, confidence), the refusal path, and the modeling limitations, so an agent has what it needs to call and interpret it.

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 already 100%, setting the baseline at 3, but the description adds real meaning: it distinguishes tokens_per_day from the requests_per_day + prompt/output token path, explains tokens_per_second as a user-supplied override of the estimate, and clarifies the compact/full detail trade-off.

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 precise decision question (self-host an open-weights model vs cheapest API) and enumerates exactly what is returned (verdict/headline, break-even volume, required throughput, GPU config, confidence). This is clearly distinguishable from siblings like estimate_cost and cheapest, which price rather than compare hosting strategies.

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?

Explains the operational conditions well: it works with or without a volume, tokens_per_second overrides the estimate, and it declines with 'status refused' for closed models or unfit GPUs. It does not, however, explicitly route the agent away from or toward sibling tools (e.g., when to prefer estimate_cost or gpu_rentals).

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