Gravity AI UI

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

Beyond annotations, the description discloses notable behavioral details: it 'calls the public generator with the server OpenAI configuration' and 'does not save or publish the result.' This adds valuable context about external calls and persistence, though it does not cover all potential side effects or error conditions.

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 two sentences, direct, and front-loaded with the primary action and target. Every clause contributes meaningful information without redundancy or 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 tool with two simple parameters and no output schema, the description covers the main purpose, invocation context, and key behavioral caveats (no save/publish). It could briefly mention return behavior or alternatives, but the core usage is adequately complete for an agent.

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 already provides 100% coverage for both parameters, including descriptions for 'prompt' and 'conversationId.' The tool description adds little semantic detail beyond the schema, only mentioning 'from a prompt,' which is already present in 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: 'generate a new Gravity AI UI composed interface from a prompt.' It distinguishes itself from siblings like get_interface, refine_interface, and search_interfaces by focusing on creation from a prompt rather than retrieval or refinement.

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 opens with 'Use to generate a new...' providing clear context for when to invoke it. However, it does not mention when not to use it or explicitly name alternatives among the sibling tools, leaving some ambiguity for an agent comparing options.

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 indicate read-only, idempotent, and non-destructive behavior. The description adds specific return contents (metadata, composed payload, thumbnails, page URL, React code) and notes the data is 'sanitized public', adding context beyond the structured fields. It doesn't cover error cases or pagination, but the tool is simple with one id.

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, front-loaded sentence that efficiently communicates the exact use case and expected outputs. 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 simple one-parameter nature, strong annotations, and the description explicitly enumerating the return data, the tool is fully specified. No output schema exists, but the description covers what the agent can expect to receive.

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% and the parameter 'id' is described as 'Public Gravity AI UI gallery interface id' in both the schema and the tool description. The description adds no extra meaning or examples beyond what the schema already provides, so the baseline score of 3 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 clearly states the tool's function: given a public Gravity AI UI gallery id, it returns sanitized interface metadata, composed payload, thumbnail URLs, page URL, and copyable React code. It distinguishes from siblings (generate, refine, search) by focusing on retrieval of an existing interface by id.

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 a clear condition for use ('when you already have a public Gravity AI UI gallery id'), implying that this is not for finding or creating interfaces. However, it doesn't explicitly name alternatives or exclusions, though the sibling list and phrase 'already have' offer context.

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 a key behavioral detail beyond the annotations: 'does not save or publish the result.' This is important given readOnlyHint=false, clarifying that while the tool may modify in-memory state, it does not persist changes. It also implies the output is a refined payload. There is no contradiction with 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 two concise sentences, front-loaded with the action verb 'Use to refine.' Every sentence earns its place: the first states the purpose and target, the second clarifies a crucial side effect (no persistence). There is no redundant or filler content.

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 adequately covers the tool's purpose and a key behavior for a refinement tool, despite having five parameters and no output schema. It leverages the schema descriptions for parameter details and sibling context for differentiation. The main gap is not explicitly mentioning the return value, but 'refine' implies an updated payload, making this sufficiently 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 the baseline is 3. The description adds context by naming 'natural-language instruction' and 'previously generated payload,' which maps to the required parameters, but it does not provide additional syntax or constraint details beyond what the schema already offers. No parameter information is missing, but the description does not enrich the 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 clearly states the tool's function: refining a previously generated Gravity AI UI composed payload using a natural-language instruction. It distinguishes itself from siblings by emphasizing 'previously generated,' aligning with the presence of generate_interface, get_interface, and search_interfaces. The phrase 'does not save or publish' adds a clear boundary for the tool's effect.

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 a clear when-to-use context: when you have an existing composed payload to refine. It implies this is not for creating new interfaces (sibling generate_interface) or for retrieval (get/search). However, it does not explicitly state exclusions or name alternative tools, so it lacks the full 'when-not-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 provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's job is lighter. It adds context by stating 'This returns public gallery metadata only,' clarifying the output scope and the limitation that it does not return full payloads or React code. 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 three sentences, front-loaded with the primary purpose, and each sentence earns its place. It is concise, clear, and includes a useful pointer to a sibling tool without unnecessary detail.

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 two optional parameters, no output schema, and rich annotations, the description is complete. It explains what the tool searches, what it returns, and when to use an alternative. The absence of return format details is acceptable for a search 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 coverage is 100%, so the baseline is 3. The description adds 'or id' to the search fields, but the schema says 'query' is matched against titles and summaries only, which introduces ambiguity. It does not significantly elaborate on the 'limit' parameter beyond what the schema states.

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: 'discover public liked Gravity AI UI interface drafts by title, summary, or id.' It uses a specific verb (discover/search), identifies the resource (public liked UI interface drafts), and distinguishes itself from get_interface by noting it returns metadata only.

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 provides usage guidance by stating 'Use get_interface when you need the full payload or React code for a known id.' This clearly differentiates when to use this tool versus an alternative, and implies this tool is for discovery/search rather than retrieval.

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