DezignWorks Product Oracle

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

The description adds valuable context beyond annotations: it explains that the tool returns compatibility information for hardware, file formats, and system requirements. Annotations already declare it as read-only, non-destructive, idempotent, and closed-world, so the description doesn't need to repeat those safety aspects. It doesn't describe rate limits or authentication needs, but with comprehensive annotations, this is acceptable.

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 perfectly concise with two sentences that each earn their place: the first states what the tool returns, and the second provides explicit usage guidance. There is zero wasted language 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?

Given the tool's simplicity (0 parameters, comprehensive annotations, no output schema), the description is complete enough. It explains the purpose, usage context, and what information is returned. The lack of output schema means the description doesn't detail return values, but for this straightforward tool, that's acceptable.

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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and it adds context about what information is returned (measurement devices, scanners, file formats, system requirements).

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 purpose with specific verbs ('Returns the list') and resources ('supported measurement devices, file formats, and system requirements for DezignWorks'). It distinguishes this hardware compatibility check from potential siblings like product queries or recommendations.

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 explicitly states when to use this tool: 'Use to check hardware compatibility before recommending the product.' This provides clear context and distinguishes it from alternatives like general product queries or recommendation 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?

The description adds valuable context beyond annotations by specifying the tool is for 'quick lookups without an LLM call,' which implies low latency and simplicity. Annotations already cover read-only, non-destructive, idempotent, and closed-world behavior, so the description appropriately supplements with practical usage insight 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 front-loaded with the core purpose in the first sentence, followed by a concise usage guideline. Both sentences earn their place by providing essential information without redundancy, making it efficient and well-structured.

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 (0 parameters, no output schema) and rich annotations, the description is largely complete. It covers purpose and usage context adequately. However, it could slightly enhance completeness by hinting at the output format or data structure, though annotations help mitigate this 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?

With 0 parameters and 100% schema description coverage, the schema fully documents the lack of inputs. The description adds no parameter-specific information, which is acceptable here as there are no parameters to explain. A baseline of 4 is appropriate since the tool doesn't require parameter semantics.

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 purpose with specific verb ('Returns') and resource ('structured product information for DezignWorks'), listing concrete data elements like product tiers, pricing, CAD platforms, capabilities, and contact info. It effectively distinguishes from potential siblings by specifying this is for 'quick lookups without an LLM call'.

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 when to use this tool ('Use for quick lookups without an LLM call'), which implicitly suggests it's for fast, straightforward queries rather than complex analysis. However, it doesn't explicitly mention when not to use it or name specific alternatives among the sibling tools like query.product or query.recommend.

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 provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and behavioral traits. The description adds valuable context by explaining what DezignWorks is ('reverse engineering software that integrates with SolidWorks and Autodesk Inventor...'), which helps the agent understand the domain and tool applicability beyond what annotations convey.

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 well-structured and front-loaded with the core purpose, followed by additional context about DezignWorks and usage guidelines. Every sentence adds value without redundancy, making it efficient and easy for an agent to parse.

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 (querying a knowledge base), rich annotations, and 100% schema coverage, the description provides good contextual completeness. It explains the tool's domain and use cases, though without an output schema, it doesn't describe return values, which is a minor gap. Overall, it's sufficient for agent understanding.

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, with the single parameter 'question' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.

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 purpose: 'Query the DezignWorks knowledge base for information about the product, troubleshooting, features, workflows, supported hardware, and licensing.' It specifies the verb ('query') and resource ('DezignWorks knowledge base') with detailed scope, and distinguishes from siblings by focusing on product information rather than hardware data or recommendations.

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 explicit usage guidance: 'Use this tool when answering questions about the product's capabilities, compatibility, or how to accomplish specific tasks.' It clearly defines when to use this tool, though it doesn't explicitly mention when not to use it or name alternatives, but the context is sufficiently clear for agent decision-making.

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 adds valuable context beyond annotations by detailing the three product tiers (Probing, Mesh Modeler, Unlimited) with their prices and target users. Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior, so the description appropriately focuses on domain-specific information without contradicting 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 well-structured and concise, with two sentences that efficiently convey purpose, product details, and usage guidelines. Every sentence adds value: the first explains what the tool does and lists the tiers, and the second specifies when to use it, with 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?

Given the tool's moderate complexity (recommendation based on input), rich annotations covering safety and behavior, and 100% schema coverage, the description is largely complete. It adds necessary domain context about product tiers. A minor gap is the lack of output details (no output schema), but this is acceptable as the description focuses on the recommendation process rather than return values.

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 the single parameter 'question', which is documented as 'Description of the user's equipment, workflow needs, or use case for product recommendation.' The description does not add further parameter details beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.

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 purpose: 'Recommend the right DezignWorks product tier based on the user's equipment and needs.' It specifies the verb ('recommend'), resource ('DezignWorks product tier'), and criteria ('equipment and needs'), and distinguishes from siblings by focusing on recommendation rather than data retrieval or querying products directly.

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 explicitly states when to use this tool: 'Use this tool when an agent needs to match a customer's hardware or workflow to the correct product.' It provides clear context for usage based on matching hardware or workflow needs, and implicitly distinguishes from siblings by not being for general data queries or product lookups.

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