LLM Hosting Pricing

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

With no annotations, the description carries the full burden. It mentions the tool returns 'cheapest GPU rentals right now' and is filterable, but does not disclose return format, sorting, or any limitations (e.g., data sources, pagination). Minimal disclosure beyond basic purpose.

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 sentence of 11 words, all essential. It is front-loaded with the main action and resource, then adds the optional filter. No wasted words.

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 tool with one optional parameter, the description is minimally adequate. However, it lacks details about the output (e.g., what fields are returned, whether it's a list, pricing info). Since there is no output schema, the description should compensate but does not.

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% for the only parameter. The description adds that the parameter is 'optional' and frames it as a filter ('optionally filtered by'), which clarifies its role beyond the schema's 'minimum VRAM in GB, e.g. 80'. This is a meaningful addition.

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 it returns the cheapest GPU rentals and mentions an optional VRAM filter. It distinguishes from sibling tools like 'list_gpus' (which likely lists all GPUs) and 'get_model_prices' (which might give specific model prices).

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 implies usage for finding current cheapest rentals, but does not explicitly state when to use this tool versus alternatives like 'get_model_prices' or 'search_models'. No when-not-to-use or exclusion criteria are provided.

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 full burden. It transparently discloses the return data (prices per provider, per 1M tokens, context window) and ordering (cheapest first). For a read-only query tool, this is sufficient, though it does not mention authentication 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?

The description is a single sentence that covers all essential information without any extraneous words. It is front-loaded with the main purpose and details follow logically.

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?

Given the tool's simplicity (one parameter, no output schema) and the context signals, the description adequately covers what the tool does and what it returns. However, it lacks details on error handling or case sensitivity, which would make it more robust.

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 single parameter 'model' has 100% schema coverage. The description adds meaningful context beyond the schema by specifying the format ('slug or name') and providing an example ('deepseek-v4-flash'). This helps the agent format the input correctly.

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 specifies the exact resource (LLM model prices), includes the data points (input/output/cache price per 1M tokens, context window per provider), and ordering (cheapest first). It clearly distinguishes from sibling tools which focus on GPUs and model search.

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 implies usage for querying prices for a specific model but does not explicitly state when to use this tool versus alternatives or context about prerequisites. Given the sibling tools are entirely different domains, the lack of explicit guidance is acceptable but still a 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?

No annotations provided, so description carries full burden. It mentions data is daily since 2026-07-06, but does not disclose behavior like error handling, output format, or what a missing GPU entails.

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?

Single sentence, concise, and front-loaded. Could add more structure but efficient.

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?

Description covers purpose and time range but lacks details on return format, pagination, or error cases. Adequate for simple tool but not fully complete given no output schema.

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 minimal context (daily, since date) but does not elaborate on parameter usage beyond schema.

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?

Description clearly states it returns daily minimum rental price history for a GPU, specifying the resource and action. It does not explicitly differentiate from siblings like 'get_model_prices', but the purpose is clear and specific.

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 guidance on when to use this tool versus alternatives such as 'cheapest_gpu' or 'get_model_prices'. The description lacks any context for selection.

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 burden of behavioral disclosure. It mentions 'live rental pricing' implying real-time data but lacks details on rate limits, pagination, or what 'cheapest' means (per second?). Adequate but not rich.

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?

Single sentence, no fluff. Every word adds value. Perfectly concise.

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?

Given no output schema, the description explains the return content (GPUs with pricing details). However, it omits sorting order or limits, which would be useful for a list tool. Still fairly complete for a simple listing.

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?

No parameters exist (0 params, 100% schema coverage). The description is not required to add parameter info. Baseline for 0 params is 4, and the description meets that.

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 lists all GPUs with live rental pricing, including cheapest $/hr, provider, tier, and VRAM. This verb+resource specification distinguishes it from siblings like 'cheapest_gpu' which likely returns only one entry.

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 explicit guidance on when to use this tool versus alternatives like 'cheapest_gpu' or 'search_models'. The description implies it's for a full list, but doesn't provide context for selection.

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 description discloses the key behavioral traits: it returns matching models, provider count, and cheapest prices per 1M tokens. Since no annotations are present, this description carries the full burden and does so adequately, though it omits details like pagination 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?

Two sentences, each serving a clear purpose: action and return value. No wasted words, highly efficient.

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?

Given the tool has only one required parameter and no output schema, the description sufficiently states what the tool does and returns. However, it does not mention potential limitations like pagination, which would add minor completeness.

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 input schema covers the single parameter 'query' with a description. The tool's description adds context by mentioning the search is by name and provides examples ('deepseek', 'llama 70b'), adding value beyond the schema.

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 action ('Search LLM models by name') and the resources involved (LLM models). It distinguishes from sibling tools like cheapest_gpu and get_model_prices which focus on GPU or 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?

The description implies usage for searching models by name, but does not explicitly state when to use this tool versus alternatives like cheapest_gpu or get_model_prices. No when-not-to-use or exclusion criteria provided.

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