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Vee3

by io.github.Vee3io

image-generation.edit

Edit an existing image or compose several images into one scene with Muse Image.

The user or agent passes stored file names from files.list_uploaded_files and/or public http or https image URLs. One image is an edit; several images are composed into one result.

The result is saved to account file storage. The response includes a signed URL for API users and download_code for agents to run vee3-get-file.

Cost = 50 tokens per generated image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of images to generate (1 to 10). Each returned image costs 50 tokens.
sizeNoOptional output size as WIDTHxHEIGHT (for example 1024x1536) or auto. This sets aspect ratio, not exact pixel size. Omit for the default aspect ratio.
imagesYesSource images as stored file names from files.list_uploaded_files and/or public http or https image URLs. One image is an edit. Several images are composed into one scene. Upload files with files.upload_file first.
promptYesText description of the image to generate or of the edit to apply.
file_nameNoOptional account-relative storage path for the image. When n is greater than 1, an index is inserted before the extension (for example art/fox.webp becomes art/fox-1.webp and art/fox-2.webp). If omitted, the file is stored under image-generation/ with a generated name.
output_formatNoEncoding of the returned image. webp is smaller; png is lossless.webp

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoEcho of the requested size when one was passed.
imagesNoGenerated images stored in account file storage.
promptNoEcho of the requested prompt.
statusNoAlways "completed" for a successful generation.
created_atNoISO 8601 timestamp.
token_costNoTokens charged for this request (50 times image_count).
image_countNoNumber of images returned and billed.
generation_idNoUnique identifier, prefix igen_.
output_formatNoEncoding of the stored images (webp, png, or jpeg).
install_commandNoOne-time command to install the Vee3 CLI (`npm install -g @vee3/cli`). On networks that inspect HTTPS, install may require Node 22.15+ with NODE_OPTIONS=--use-system-ca.
troubleshootingNoWhat to do if installation or downloading fails: re-read this tool's description via meta-tools.describe for setup and troubleshooting steps.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description discloses key behaviors: the tool saves results to account file storage, returns a signed URL and download_code, and costs 50 tokens per image. It does not deeply explain failure modes or exact response structure, but the essential runtime 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.

Conciseness5/5

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

The description is compact, front-loads the primary purpose, and each sentence adds distinct value: input sources, edit-vs-compose semantics, output delivery mechanics, and cost. No fluff or repetition of schema details.

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?

The description, combined with the rich 100%-coverage input schema and output schema, fully equips an agent to call the tool correctly. It covers input sourcing, parameter behaviors, output access, and cost. No significant gaps remain.

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 description coverage is 100%, so the schema already documents all six parameters in detail. The description adds operational context beyond the schema by explaining the one-image-edit vs multi-image-compose behavior and the download_code retrieval mechanism, which complements the parameter docs.

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?

The description clearly states the tool edits an existing image or composes multiple images, explicitly distinguishing single-image edit vs multi-image composition. It names the underlying model (Muse Image) and differentiates from the sibling image-generation.generate tool.

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?

The description explains the source inputs (stored file names from files.list_uploaded_files and/or public URLs), the one-vs-many behavior, and how to retrieve the output (download_code for vee3-get-file). It also notes the token cost, helping an agent decide when to use it.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct purpose, further clarified by group prefixes and clear descriptions. Within each group, tools perform different operations (e.g., domains.lookup vs. domains.whois vs. domains.rdap) with no ambiguity.

Naming Consistency5/5

All tools follow a consistent group.tool_name pattern using snake_case. The naming is predictable and uniformly applied across all groups.

Tool Count4/5

78 tools is high, but the server aggregates multiple distinct API domains (11 groups). Each group has a reasonable number of tools, typically under 10, with TikTok having 17. The count reflects breadth, not bloat.

Completeness5/5

Each domain's tool set covers the primary expected operations (e.g., search, details, reviews, metrics, user info). There are no obvious gaps for read-only analytical use; features like posting are likely out of scope.

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