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

With no annotations, the description carries the full behavioral burden. It does disclose that the call is asynchronous by returning a 'Task ID' plus image URLs, which is real value beyond the schema, but it omits what an agent most needs here: that the task must be polled via the sibling flux_get_task, and whether the source image is modified or a new output is produced. Permission, cost, and rate-limit behavior are also unaddressed.

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 definition is front-loaded with the core action and uses a scannable bullet list for use cases plus a clear Returns section. The second sentence ('This allows you to modify an existing image based on a text description') largely restates the first and could be cut, costing it a point.

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?

An output schema exists, so the Returns section is a bonus rather than a necessity, and the sibling routing is handled. The one meaningful gap for a five-parameter, async, generation-billed tool is the absence of any pointer that results are retrieved with flux_get_task, leaving an agent to infer the polling workflow.

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 parameters — including model choices, aspect-ratio sizing, and the callback webhook — are already fully documented in structured fields. The description adds nothing parameter-specific beyond reiterating that the kontext models suit editing, which the model enum description already states. 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?

The opening line states a specific verb and resource ('Edit an existing image using Flux with a text prompt') and the closing line explicitly distinguishes it from the sibling flux_generate_image. An agent can route between editing and generation without opening either schema.

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 provides an explicit 'Use this when' list covering the main edit intents (transform, element changes, style transfer, object add/remove/replace) and names the alternative tool for the generation case. Both when-to-use and when-not-to-use are 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?

No annotations are supplied, so the description carries the full burden; it does disclose that the call returns a task ID plus image URLs, implying an asynchronous task lifecycle, and the schema exposes a callback_url for async notification. What it omits is any guidance to poll with flux_get_task, cost/rate-limit behavior, or how long results persist — meaningful gaps for an async generation job.

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?

Front-loaded with the action and well organized into when-to-use bullets and a returns note. The middle paragraph about Flux being a family of models is generic marketing context that partially overlaps the model enum descriptions in the schema, but it is short enough not to be wasteful.

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?

An output schema exists, so return values need not be spelled out, yet the description never mentions the asynchronous task flow or the sibling flux_get_task that an agent needs to fetch results. For a 5-parameter async generation tool with several model variants, it is adequate but leaves the agent's follow-up path implicit.

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%: size presets vs. aspect ratios per model family, the model enum, count, and callback_url are all documented in the schema. The description adds no parameter-level meaning beyond that, so the 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 and resource ('Generate AI images from a text prompt using Flux') and explicitly distinguishes itself from flux_edit_image for the editing case. An agent can route between the two siblings without opening either schema.

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?

Provides an explicit 'Use this when' list of triggering conditions and names the alternative tool (flux_edit_image) with the condition that selects it. This is the strongest form of routing 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?

No annotations exist, so the description carries the full burden. It discloses the async/polling implication and warns that draft availability is temporary, which is real behavioral value, but it says nothing about auth/permissions, cost, rate limits, or what async=false does.

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 sentences, front-loaded with the capability and ending with the follow-up action. No filler. Slightly compressed phrasing ('owned temporary draft') costs a little clarity.

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?

An output schema exists so return values need not be explained, and the polling hint covers the async lifecycle. However, the four-way oneOf with zero schema descriptions and no textual explanation of mode-specific required fields leaves genuine gaps for a fairly complex 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?

Only one top-level parameter ('request'), but it is a oneOf over four nested request shapes with 0% description coverage, so the schema supplies no prose. The description sketches the mode space but does not explain per-mode requirements (keyframes, start_video, duration caps, safety_tolerance).

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 clear verb+resource (generate video) and enumerates the supported input modes (text/image/video-to-video) plus the draft-enhance variant, which maps onto the schema's mode discriminator. It distinguishes the tool from flux_generate_image, though it does not name that sibling explicitly.

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?

'Poll flux_get_task' gives useful post-call guidance for the default async flow, but the description never says when to pick draft_enhance versus a generate mode, or when async=false is appropriate. Usage is implied 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?

No annotations, so the description carries the full burden. It discloses the async polling role and that results contain image/video URLs and metadata, but says nothing about the possible task states, whether results expire, or auth requirements — real gaps for a no-annotation tool, though the core behavior is conveyed.

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?

Front-loaded purpose sentence followed by scannable bullets; every line is relevant. Minor redundancy in the 'Returns' block, which restates output already covered by the output schema.

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 one-parameter read tool with a full output schema and 100% schema coverage, this is nearly complete. The redundant Returns section and the missing batch-sibling routing are the only shortfalls.

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 single task_id parameter is fully documented in the schema, including its origin fields. The description adds no parameter detail beyond that, so the 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+resource: 'Query the status and result of a Flux image or video generation task.' Clear that this is a polling/retrieval tool distinct from the generation siblings, but it never differentiates itself from flux_get_tasks_batch, the obvious plural counterpart.

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?

Four explicit when-to-use bullets cover the polling scenarios (checking completion, retrieving URLs, async callback follow-up, task_id with no immediate result). No when-not-to-use guidance and no pointer to flux_get_tasks_batch for multi-task retrieval, so it stops short of 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?

No annotations are provided, so the description carries the full behavioral burden. It confirms this is a read-style status check returning 'status and result information for all queried tasks', but discloses nothing about permissions, rate limits, or failure behavior for missing/invalid task IDs. Adequate but not rich.

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?

Front-loaded with the core purpose, then a scannable usage list and a short returns note. The bullet list is somewhat redundant ('check status of several images' vs 'tracking a batch'), keeping it just short of a 5.

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 one-parameter read tool with an output schema present, the description covers purpose, usage, and a return-value hint without needing to detail the response shape. Nothing critical to correct invocation is missing.

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%, and the schema itself documents the single task_ids parameter including the recommended max batch of 50. The description adds no parameter-level detail beyond the schema, so the 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 and resource ('Query multiple Flux image or video generation tasks at once') and explicitly differentiates from the sibling flux_get_task by name. An agent can immediately tell it is the batch variant of a task-status query.

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?

Provides an explicit 'Use this when' list of three qualifying scenarios and directly names the alternative ('More efficient than calling flux_get_task multiple times'), so the routing decision is unambiguous.

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 bears the full burden. It states the return type ('categorized list of all tools with descriptions') but doesn't disclose behavioral traits like no side effects, idempotency, or performance characteristics. For a list operation, this is adequate but not exemplary.

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 sentences, no waste. The first sentence immediately states the core purpose, the second explains its role, and the third describes the return. Front-loaded and efficient.

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 no parameters and the presence of an output schema, the description sufficiently explains what the tool does and what it returns. It is complete for a simple listing 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?

The input schema has zero parameters, and the description adds value by confirming that it lists 'all' available tools, implying no filtering options. With 100% schema coverage, the description reinforces the simplicity.

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 'List all available Flux tools and their use cases,' which is a specific verb-resource combination. It distinguishes from sibling tools like flux_generate_image and flux_list_models, which have different purposes.

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 frames it as a 'reference guide for what each tool does and when to use it,' implying it should be used to understand other tools. While it doesn't explicitly state when not to use it, the sibling context makes its utility clear.

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 fully bears the burden of disclosure. It adequately describes the behavior: listing models with capabilities and recommendations, implying a read-only, non-destructive 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 concise with three short sentences covering what, why, and return. It is front-loaded with the primary action and adds value without verbosity.

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 no parameters and an output schema, the description is fairly complete. It explains the purpose, return value, and use case, though it could explicitly state it is read-only.

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?

There are no parameters, and schema coverage is 100%. The description adds context by stating the return content (detailed list with descriptions and recommendations), which is not 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 that the tool lists all available Flux models and their capabilities, with a specific verb (List) and resource (Flux models). This distinguishes it from sibling tools that edit, generate, or retrieve tasks.

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 mentions it is a 'Reference guide for choosing the right Flux model for your use case,' implying usage before model-dependent operations, but it does not explicitly exclude other uses or mention alternative tools.

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