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

No annotations are provided, so the description carries the full burden. It discloses one useful constraint (premium plan required), but says nothing about whether generation is async (sibling wait_for_generation hints it is), rate limits, or what the call returns.

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?

Short and front-loaded, but the bare endpoint path 'POST /uapi/v1/imagen/gpt-image-2' is noise that consumes space without helping the agent, and the plan requirement is tacked on rather than integrated.

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 7-parameter tool with 14% schema coverage, no annotations, and no output schema, the description is far too thin; an agent gets no guidance on optional inputs, async behavior, or the return value.

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 only 14% — just one of seven parameters (resolution) is documented. The description adds no meaning for mode, file_urls, background, ref_history, or aspect_ratio, so it fails to compensate for the coverage gap.

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+resource ('Generate an image') and names the underlying model 'GPT Image 2', which meaningfully distinguishes it from siblings generate_image and generate_grok_image. The raw endpoint URLs add nothing an agent needs, but the core purpose is unambiguous.

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 only routing signal is the implicit model choice; the description never says when to pick this over generate_image or generate_grok_image, nor when a premium plan makes it unavailable. It notes 'Premium plan required by snapgen', which is a prerequisite but not usage 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?

With no annotations, the description carries the full burden of behavioral disclosure. It only restates the action and supplies a POST endpoint; it does not say whether generation is synchronous or asynchronous, what authentication is required, whether there is a cost, or what the response looks like—especially important given the sibling wait_for_generation tool implies async workflows.

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 short, front-loaded sentences with no filler. The endpoint sentence is a minor technical detail that may not aid tool selection, but the overall structure is compact and readable.

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 six-parameter image-generation tool with no annotations and no output schema, the description omits critical context: mode differences, num_result, orientation, file_urls, ref_history, and async behavior. It is not complete enough to let an agent invoke the tool confidently.

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 0% and six parameters (prompt, mode, file_urls, num_result, orientation, ref_history) are completely undocumented in both the schema and the description. The description adds no parameter-level meaning, such as the distinction between SPEED and QUALITY or how ref_history or file_urls are used.

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 ('Generate an image with Grok'), and naming Grok differentiates it from the sibling generate_gpt_image. However, it does not distinguish this tool from generate_image or explain when to prefer one over the other.

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?

There is no explicit when-to-use guidance, no prerequisites, and no routing advice among the multiple image-generation siblings (generate_image, generate_gpt_image). The reader is left to infer usage entirely from the tool name and provider.

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 full burden. It does disclose the key async behavior — returns a job rather than the image, poll to retrieve — which is real value. It omits auth requirements, cost/credit implications, latency expectations, and failure modes for a remote generation call.

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?

Three short sentences, front-loaded with purpose and followed by the async contract and polling route. The raw endpoint path is mildly redundant for an agent, but nothing is padded.

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 an asynchronous job-creating tool with no output schema, the description correctly conveys that the return value is a job handle and names the polling tool, which is the critical missing piece. Parameter-level and auth detail remain thin, but the call pattern is complete enough to invoke and follow up.

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 57%, below the threshold where the schema can be assumed to do the work, and the description adds no parameter meaning at all. Notably the model and resolution enums carry no descriptions, and the distinction between file_urls, ref_history, and style is left entirely to 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?

States a specific verb (Generate) and resource (image) and pins down the model family ('snapgen nano-banana models'), which implicitly separates it from siblings generate_gpt_image and generate_grok_image. It does not explicitly name those siblings, so it stops short of a 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?

The description gives the follow-up path clearly ('poll with get_generation'), which is genuinely useful routing. However it never says when to choose this tool over generate_gpt_image or generate_grok_image, so the core selection decision 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?

With no annotations provided, the description carries the full behavioral burden. It states the tool returns a uuid and implies asynchronous job submission, but says nothing about authentication, rate limits, cost, whether the job can be cancelled, or what the polling contract looks like – all material for a generation tool.

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 short sentences with the job-submission action front-loaded, and the return contract stated inline. No wasted words, though the second clause about family is under-specified rather than 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?

For an 8-parameter, asynchronous generation tool with no output schema and no annotations, the description omits too much: parameter meanings, model/family relationship, duration and resolution constraints, and polling details are not covered.

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 only 63%. The description adds no semantics for prompt, model, mode, duration, file_urls, resolution, or aspect_ratio; the sentence about family is vague ('documented path') and does not explain the enum values or how family relates to model.

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?

It identifies a submission action for video jobs and names the return value, but the awkward phrase 'family selects the documented path' obscures the primary function and does not differentiate it from the sibling image-generation tools beyond the word 'video'.

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?

It mentions polling with get_generation, which implies follow-up flow, but there is no guidance on when to use this tool versus generate_image or the other generate_* siblings, nor any stated 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 burden. It usefully discloses the underlying endpoint and the semantics of the status values in the response, which is real behavioral context. It still omits what else a completed generation returns (e.g. asset URLs) and whether a missing uuid errors or returns empty.

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?

Three short, front-loaded fragments with no wasted words; the core action comes first. The status list is terse to the point of being slightly cryptic, with no delimiters or explanation of where those codes appear.

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?

There is no output schema, so the description is the only source for return semantics, and it covers only the status field. For a fetch-by-id tool, the agent should also know what a completed payload contains and how failures surface; that gap keeps it at minimum-viable.

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 self-documenting parameter named 'uuid' at 0% schema description coverage. The phrase 'by uuid' confirms the identifier's role but adds nothing about format (e.g. that it must be a full UUID string) or error behavior for an unknown id.

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 and resource: 'Fetch one generation by uuid', and 'one' implicitly contrasts with the sibling list_generations. It does not explicitly name an alternative, but the singular scope plus the lookup key makes the purpose unambiguous.

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 when-to-use or when-not-to-use guidance, and the polling sibling wait_for_generation is never mentioned. However, the status code list ('1 processing, 2 completed, 3 failed') implies the intended use of checking whether a generation has finished, so usage is inferable rather than stated.

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 full behavioral burden. It only notes 'GET /uapi/v1/histories', which implies a read operation, but does not disclose pagination behavior, ordering, authentication requirements, rate limits, or what 'recent' means. This is insufficient for a list operation.

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 very concise and front-loads the core purpose. The second sentence, the endpoint path, is arguably an implementation detail with limited value for an agent invoking the tool, but it does not create bloat.

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 three parameters, no annotations, no output schema, and low schema description coverage, the description is incomplete. It omits parameter semantics, usage context, pagination details, and return information, leaving significant gaps 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?

Schema description coverage is 33%, with only 'filter_by' having a brief description. The description does not compensate for undocumented parameters like 'page' and 'items_per_page', nor does it clarify the meaning or accepted values of 'filter_by' beyond what the schema already 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 states a clear verb ('List') and resource ('recent generations'), making the tool's purpose immediately understandable. It does not explicitly differentiate from siblings like get_generation or the generate_* tools, but the verb and scope distinguish it adequately.

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 is provided about when to use this tool versus alternatives such as get_generation (for a specific generation) or the generate_* tools. The description offers only the basic purpose, leaving the agent to infer usage scenarios.

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 burden of behavioral disclosure. It says nothing about authentication, rate limits, whether calls may mutate or delete data, or what errors look like. It only implies a generic HTTP call, leaving significant safety and side-effect behavior undocumented.

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 short and front-loaded, with the tool's role stated first. The final sentence repeats a constraint already in the schema, which is minor redundancy but does not bloat the text.

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 generic HTTP passthrough with no annotations and no output schema, the description omits critical context: how method, query, and fields interact, what side effects are possible, and how responses or failures should be interpreted. The usage context is helpful but insufficient for correct and safe invocation.

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 only 50%, and the description repeats the path constraint ('path must start with /uapi/') that is already present in the schema. It adds no meaning for query, fields, or method, leaving half the parameters without useful semantic guidance.

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 generic verb (call) and resource (any snapgen path under /uapi), but it is effectively a catch-all proxy and does not name a specific operation. The fallback context ('when a model ships before this server is updated') gives some differentiation from the specific generation siblings, but the core purpose remains vague.

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?

It explicitly says when to use the tool: when a model ships before this server is updated. It does not state when not to use it or name the specific sibling alternatives, but the context is clear enough for an agent to understand the fallback role.

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 full behavioral burden. It usefully discloses the polling loop, the three termination states, and that it will not resubmit — but omits whether the call blocks, what is returned on failure vs timeout, and any rate/concurrency constraints.

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 tightly-worded sentences with the core behavior front-loaded and zero filler. Nothing can be trimmed without losing meaning.

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 3-parameter polling tool with no annotations and no output schema, the description covers the concept but leaves gaps: what the call returns on each terminal state and what interval_seconds/timeout_seconds actually govern are unaddressed.

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 0% and the description adds no parameter meaning beyond the word 'timeout'. The uuid, timeout_seconds, and interval_seconds semantics (and the polling interval in particular) are left entirely to the bare schema, which only shows types, defaults, and ranges.

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 (poll/wait) and resource (a generation) with the terminal conditions. It distinguishes itself from the generate_* siblings by clarifying it waits rather than creates, though it never names get_generation as the immediate-check alternative.

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?

'Does not resubmit the job' hints at how it differs from generate_* tools, and 'until it completes, fails, or the timeout is hit' implies the blocking use case. However, it gives no explicit guidance on when to prefer this over get_generation or list_generations.

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