GPT Image Hub

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

With no annotations, the description carries the full burden, and it does disclose that the output is a browser URL and that behavior differs between prompt-preload and freeform modes. It does not state whether the operation has side effects, whether it requires the prompt to be public/accessible, or how errors surface, leaving real behavioral gaps.

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?

A single compact sentence that front-loads the action and then the two supported modes. Every clause earns its place with no 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?

For a two-optional-parameter tool with full schema coverage and no output schema, the description is nearly sufficient: it explains the tool's purpose and both usage modes. Minor gaps remain around locale behavior and the exact semantics of omitting promptSlug.

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 both optional parameters are already documented in the schema. The description adds only loose mapping ('public prompt' to promptSlug) and says nothing about locale, so baseline 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 states a specific verb and resource ('Create a browser URL') and clarifies the two outcomes the URL can produce: preloading a public prompt or opening freeform generation. It is clear on its own, though it does not explicitly distinguish itself from siblings like get_prompt or search_prompts.

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 by the two modes described: provide a prompt to preload, or omit it for freeform generation. However, it never states when an agent should prefer this tool over get_prompt or search_prompts, nor any exclusions or prerequisites.

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 the full behavioral burden. It discloses that only 'public' templates are reachable, which is useful, but says nothing about what happens when a slug is missing, error behavior, or auth requirements for a fetch-by-identifier lookup.

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?

A single front-loaded sentence with zero filler. It is efficient, though its brevity is part of why usage and behavioral detail are absent.

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 two-parameter read lookup this is close to adequate, and no output schema exists so return values need not be described. However, with no annotations and no output schema, the description should at least note the form of the result or failure behavior.

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 both slug and locale, making 3 the baseline. The description reinforces 'by slug' but adds no meaning about the optional locale parameter or its effect.

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 (fetch), resource (public GPT Image Hub prompt template) and scope (one, by slug). It implicitly contrasts with search_prompts, though it never names the sibling, so an agent must infer the distinction from 'one' vs '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 gives no when-to-use guidance, no exclusions, and does not mention search_prompts as the alternative for discovering prompts. The 'by slug' phrasing weakly implies you must already know the slug, but nothing is stated explicitly.

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, so the description carries full burden. "public" usefully discloses the visibility scope, but there is no mention of return shape, ordering, pagination, or whether the list is exhaustive.

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?

One front-loaded sentence with no filler, appropriate for a simple list tool. It is efficient, though slightly too sparse to be maximally helpful.

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 a one-param, no-output-schema tool, the description covers the essentials but omits what a returned category looks like and whether the response is localized by the locale param.

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?

Single optional parameter (locale) with 100% schema description coverage, so the schema does the documentation work; the description adds nothing about what locale affects. 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?

States a specific verb ("List") and resource ("public GPT Image Hub prompt categories"), giving a clear read-only enumeration scope. It is distinguishable from search_prompts and get_prompt, though it doesn't explicitly contrast itself with them.

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 versus search_prompts or get_prompt; the usage (discovering categories before filtering) is only implied by the word "categories." Sibling differentiation 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?

No annotations provided, so the description must carry full behavioral burden. It discloses the return format ('full prompt text') but omits whether the operation is read-only, pagination limits, authentication needs, or rate limits. Minimal disclosure.

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 economical sentences. Purpose is front-loaded before the return note. 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 search tool with four optional params and no output schema, the description partially covers output but leaves gaps: no default limit behavior, no pagination, no usage context. Adequate but incomplete.

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 75%; query, locale, and category have descriptions, while limit only has min/max. The description adds no parameter semantics beyond the schema. Given the moderate coverage and no compensating info, a baseline 3.

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 'Search' and resource 'public GPT Image Hub prompt templates', and clarifies the return 'full prompt text'. It does not explicitly differentiate from siblings like get_prompt, so a 4 rather than 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?

No when-to-use guidance or alternatives. It doesn't indicate when to search instead of retrieving a specific prompt via get_prompt or listing categories. Only implied usage from the verb.

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