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Generate or edit an image with Kavel

generate_kavel_image

Create an image from a prompt, or edit the user's photo, on their Kavel account. Pass tool (a slug from find_kavel_tool) to apply that effect's tuned recipe, with prompt holding the user's own details. Photo tools need a photo: the user's attachment in photos, or a public link in image_urls. Spends credits from the linked Kavel account. Usually returns the image within a minute; otherwise returns a task_id for check_kavel_generation.

Input Schema

NameRequiredDescriptionDefault
toolNoOptional image tool slug from find_kavel_tool.
photosNoPhotos the user attached in the chat. Required by photo tools.
promptNoThe user's description, or their extra details when a tool is given.
image_urlsNoPhotos to edit given as public http(s) links, when not attached.
aspect_ratioNoOutput image shape. Defaults to 1:1.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / aspect_ratio / description
      Added value: +"Output image shape. Defaults to 1:1."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only declare the mutation/safety profile; the description adds the operationally critical facts beyond them: it spends credits from the linked account, typically completes within a minute, and otherwise returns a task_id for async polling. Those are exactly the traits an agent needs before invoking a metered mutation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core action, then layers the tool/photo requirements, cost, and async behavior in tight sentences with no filler. Every clause carries decision-relevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a metered, potentially async generation tool with no output schema, the description covers cost, expected latency, the async escape hatch, and where the tool slug originates — everything needed to call it correctly and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description genuinely adds meaning beyond the schema: `tool` consumes a slug from find_kavel_tool and reinterprets `prompt` as the user's extra details rather than a standalone prompt. The photos-vs-image_urls distinction is reinforced too. It stops short of full coverage of aspect_ratio behavior, so not a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create an image from a prompt, or edit the user's photo') scoped to the Kavel account, which cleanly separates it from find_kavel_tool (lookup) and check_kavel_generation (polling). An agent can identify the tool's job without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit routing: take `tool` from find_kavel_tool, require a photo for photo tools (attachment in `photos` or link in `image_urls`), and fall back to check_kavel_generation when a task_id is returned. Both the when-to-use and the sibling handoffs are spelled out.

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

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