OptimToken

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

Annotations already indicate read-only and non-destructive behavior. The description adds clarity by stating prices are 'Linux on-demand list prices in USD' and instructs the agent to report prices exactly and avoid commentary, providing behavioral guidelines beyond the 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?

The description is three concise sentences: purpose, filtering options, and pricing/output instruction. It is front-loaded and every sentence contributes necessary information without redundancy.

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 an output schema present and 10 optional parameters, the description provides sufficient context about pricing basis and exact output behavior. It does not mention default sorting or limits, but these are covered by the schema. Overall, it is reasonably complete for a read-only comparison 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?

Since schema description coverage is 100%, the description does not need to explain parameters. It merely lists filter categories (provider, vCPUs, memory, etc.) that correspond to existing schema properties, adding no new semantic value.

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: 'Compare cloud compute instance pricing across AWS, Azure, GCP, DigitalOcean, OCI, OVH, and Alibaba.' This specific verb+resource distinguishes it from sibling tools like compare-llm-models and estimate-llm-cost, which focus on different aspects.

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 clear context for usage by stating it compares compute pricing and specifies filtering criteria. However, it does not explicitly reference sibling tools or state when-not-to-use, though the purpose alone differentiates it from LLM-related 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 already declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds valuable behavioral context: it fetches live data from OpenRouter, reports both list-price and optimized costs, and instructs the agent to present results as a table without adding commentary. This goes beyond the annotations and helps the agent understand output expectations.

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 four sentences, each earning its place: the first states purpose, the second covers data source and filters, the third clarifies independence of attributes and cost reporting, and the final sentence is a concise, important instruction about output formatting. It is front-loaded and has no 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?

Given the tool's complexity (10 optional parameters, an output schema, and a live-data source), the description covers all key aspects: what it compares, where data comes from, available filters, enrichment option, pricing nuance, and output instructions. With an output schema present, the description needn't detail return fields. It could mention default values for volumePreset/useCasePreset, but those are in the schema, so this is not a significant 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 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining that price tier and openness are independent (e.g., a model can be Frontier-priced and open-weight), and by grouping 'price range' to map to maxInputPrice/maxOutputPrice. It also clarifies that useCasePreset is for 'business metrics enrichment.' This semantic linking improves parameter understanding.

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 opens with a specific verb and resource: 'Compare AI/LLM models by price, quality (ELO), efficiency, and capabilities.' This clearly distinguishes the tool from sibling tools like estimate-llm-cost (which focuses on cost estimation) and compare-compute-pricing (which likely covers compute pricing). It names the data source (OpenRouter API) and the key dimensions, leaving no ambiguity about the tool's purpose.

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 clearly implies when to use the tool: when comparing LLM models across multiple dimensions. It also lists concrete filters that narrow the search. However, it does not explicitly name alternative tools for other tasks (like 'use estimate-llm-cost for single-use-case cost estimation'), so it earns a 4 rather than 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context by disclosing that 'Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API,' and by instructing the agent to avoid commentary. This goes beyond the annotations without contradicting them.

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 four sentences, front-loaded with the purpose, then usage, then output detail, then a critical reporting instruction. Every sentence adds value and there is no waste or redundancy.

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 5 optional parameters, an output schema, and annotations, the description fully covers the tool's purpose, usage modes, dual-pricing behavior, and essential reporting instruction. It is complete and self-sufficient for an agent to invoke 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?

The input schema has 100% description coverage for all 5 parameters, so the baseline is 3. The description does not add meaningful parameter semantics beyond what the schema already states (e.g., modelName's 'If omitted, shows top models' is already in the schema). The description mentions 'use cases and volumes' generically but adds no specific parameter details.

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: 'Estimate per-request and monthly costs for AI/LLM models.' It uses a specific verb ('estimate') and resource ('costs for AI/LLM models'), and distinguishes itself from sibling comparison tools by focusing on cost estimation rather than model feature comparison or generic compute pricing.

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 clear invocation context: 'Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets.' It also gives an important usage instruction to report figures exactly. However, it does not explicitly mention exclusions or alternative sibling tools, so it earns a 4 rather than a 5.

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