jimeng-ai-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct media generation or editing task: text-to-image, image-to-image, inpainting, upscaling, text-to-video, image-to-video, motion imitation, digital human creation, and video translation. There is no meaningful overlap between these operations.
Naming Consistency5/5
All tool names use a consistent lowercase_snake_case convention with a clear verb_noun or transformation pattern (generate_image, inpaint_image, image_to_video, translate_video). The two image_to_* names follow a predictable transformation pattern, making the set uniform and intuitive.
Tool Count5/5
With 9 tools, the server is well-scoped for an AI media generation platform. Each tool covers a distinct capability without redundancy, and the count falls comfortably within the ideal 3-15 range.
Completeness4/5
The tool surface covers the core media generation lifecycle: image creation/editing/upscaling, video generation from text or images, motion transfer, digital humans, and video translation. Minor gaps like audio generation or task management are not critical for the stated purpose, but the set is quite comprehensive.
Average 4.3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions the model used and the public URL requirement, but does not disclose whether the operation is asynchronous, how the result is returned, potential costs/latency, or any side effects. For a complex video processing tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description opens with a clear purpose statement, then uses bullet points for parameters and a compact list for languages. The language list is necessary but adds length; overall it's well-structured and not wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is complex (video translation with lip sync), and the description covers inputs, model, and language support. However, it omits operational details like output format, processing time, video size limits, or error conditions. Since an output schema exists, return value explanation is less critical, but other contextual 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 0%, so the description must explain parameters. It does so effectively: video_url must be publicly accessible, target_language is an abbreviation, and src_language defaults to "zh". It also provides a list of supported language codes. This adds meaning beyond the bare schema.
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's specific function: translating speech in a video into a target language while syncing lip movements. This specific verb+resource (translate video + sync lips) distinguishes it from sibling tools like generate_video or image_to_video, which create new content rather than transform existing speech.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on how to use the tool: requires a publicly accessible video URL and target language, with an optional source language. It also lists supported language codes. However, it does not explicitly mention when to prefer this tool over alternatives or when not to use it. This is close to 'clear context, no exclusions' but lacks explicit alternative differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 adds useful context by specifying the model (jimeng_image2image_dream_inpaint), the mask encoding (white=redraw, black=preserve), and special prompt behavior ('删除' to erase). However, it does not discuss side effects, reversibility, or auth requirements, which are less critical for an image editing tool but still would enhance transparency.
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 and front-loaded with a clear summary sentence, followed by a concise bulleted list for parameters. Every sentence earns its place, with no filler or redundancy. It is structured for quick parsing by an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple tool, the description covers the core behavior and input requirements thoroughly. It does not need to describe return values because an output schema exists. Minor gaps include lack of mention of output format and no explicit alternative distinction, but overall the description is nearly complete for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema offers only parameter names and types, with 0% coverage. The description compensates fully by detailing allowed formats (JPEG/PNG), size limit (4.7MB), mask semantics (single-channel grayscale, white/black meanings), and special prompt usage. This transforms bare schema into actionable guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: local inpainting (局部重绘) that regenerates content in a masked region based on a prompt. It is specific and distinct from generic image generation, but it does not explicitly differentiate itself from the sibling tool 'image_to_image', missing the opportunity to highlight the mask-based uniqueness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It neither mentions scenarios where inpainting is appropriate nor contrasts with siblings like image_to_image or generate_image. The usage context is only implied by the parameter semantics (mask), but no explicit or even implicit 'use when' or 'avoid when' statements are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It compensates well by disclosing model choice, image format/size limits (JPEG/PNG, max 4.7MB, 4096×4096), aspect ratio options, quality settings, and the semantics of the 'scale' parameter. It does not describe output formatting, but an output schema exists to cover that.
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 concise and well-structured. It opens with a clear purpose statement, then provides a compact bulleted list of parameter details. Every sentence adds value, and there is no redundant or extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with 5 parameters, and the description covers most of them thoroughly. It includes model version and input constraints. It does not explicitly differentiate from sibling tools (like inpaint_image), and the 'prompt' parameter could use a bit more elaboration, but overall it is sufficient for an agent to confidently invoke the tool.
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?
The input schema provides only type/defaults with no property descriptions (0% schema description coverage), so the description must add meaning. It explains image_url constraints, allowed aspect ratios, quality choices (2k default vs normal), and scale's effect (0.0-1.0, larger follows text, smaller preserves the original). The 'prompt' parameter is implicitly covered via '结合文字描述' but lacks explicit format details.
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's function: '图生图（智能参考）：参考输入图片，结合文字描述编辑或生成新图片' (image-to-image with smart reference: edit or generate new images based on an input image and text description). It also specifies the underlying model (jimeng_i2i_v30), making the purpose specific and distinct from siblings like generate_image or inpaint_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by emphasizing '参考输入图片' (reference input image) and '结合文字描述' (combined with text description), suggesting it is for image-conditioned generation. However, it does not explicitly state when to prefer this tool over alternatives such as inpaint_image or generate_image, nor does it provide exclusions or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full responsibility for behavioral disclosure. It adds valuable context such as the exact model, audio format and duration limits, resolution choices, prompt language constraints, and a warning that this API uses CVSubmitTask/CVGetResult rather than the normal interface.
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 concise and well-structured with bullet points for parameters and a clearly separated usage note. Every sentence provides useful information, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose, all parameters, important constraints, and the non-standard API flow. Since an output schema is present, return values need not be explained, though it stops short of detailing how to actually use CVGetResult to retrieve results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates by explaining all four parameters: portrait_url (person/anime/pet image), audio_url (WAV/MP3 under 60 seconds), resolution (720/1080 default 1080), and prompt (optional, multilingual, max 300 characters). This adds substantial meaning beyond the bare schema.
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 that this tool generates a digital human talking video by driving a portrait image's lips and actions with audio, and it names the specific model (OmniHuman 1.5). This specific verb+resource combination distinguishes it from sibling tools like generate_video or image_to_video.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The use case is implied by the phrase '数字人生成' (digital human generation) and the inputs described, but there is no explicit guidance on when to choose this tool over alternatives such as image_to_video or imitate_motion. No when-not-to-use or alternative tool references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 adds useful behavioral context: model versions with capabilities (e.g., 4.6 supports up to 4K), a default recommended model, and a caveat that negative_prompt is optional and may not be supported by all models. This goes beyond the schema, though it does not disclose rate limits, authentication, or async behavior.
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 one clear sentence followed by a concise bullet list of parameters. It is front-loaded with the purpose and avoids redundancy. Every line earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with rich model options, the description covers all parameters with defaults and options, and an output schema exists to describe return values. It lacks guidance on when to choose specific models beyond the default recommendation, but overall it is sufficiently complete for an image generation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. It enumerates valid model values with human-readable meanings, aspect ratio options, quality levels, and explains negative_prompt semantics including a support caveat. This adds substantial meaning beyond the bare input schema.
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 '文生图：根据文字描述生成图片' (text-to-image: generate an image from a text description), using a specific verb and resource. It distinguishes itself from sibling tools like image_to_image and generate_video by explicitly framing itself as text-to-image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied through the 'text-to-image' framing: use this when you have a text description and want a generated image. However, there is no explicit when-not-to-use guidance or mention of alternative tools for related tasks (e.g., image_to_image for editing existing images).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides useful behavioral context such as the approximate generation time (1–5 minutes) and model quality differences. However, it does not disclose asynchronous behavior, rate limits, or potential error conditions.
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 and front-loaded: a one-sentence purpose followed by a bulleted parameter list. Every line adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and the tool's scope, the description covers key operational details: models, aspect ratios, duration, and time estimate. Minor omissions like prompt length limits or content restrictions leave small gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions, but the description fully compensates by enumerating valid model options, aspect ratios, and duration choices (5 or 10 seconds). It adds significant meaning beyond the bare schema.
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 generates video from text (文生视频：根据文字描述生成视频), using a specific verb and resource. This distinguishes it from sibling tools like image_to_video and image_to_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear that this tool is for text-to-video generation, providing a clear context for when to use it. It does not explicitly exclude alternatives, but the input criterion (text description) is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the behavioral transparency burden. It does so admirably by disclosing an estimated duration ('约需 1～5 分钟'), image constraints (JPEG/PNG, max 4.7MB), mode resolutions (720P/1080P), and conditional requirements for tail_image_url and template_id. It stops short of explaining the async nature or result retrieval, but the output schema may cover that.
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 well-organized with a clear one-sentence purpose, followed by structured bullet points for each parameter and mode. It is slightly long but every line adds operational value, with no redundant or filler content.
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 tool with five parameters and multiple modes, the description is remarkably complete. It covers all input constraints, mode-specific behaviors, duration options, and even provides sub-list of template IDs. Since an output schema exists, not describing return values is acceptable. The only minor omission is an explicit statement about whether the operation is synchronous, but the time estimate strongly implies asynchronous.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does so fully by explaining every parameter: image_url's format and size, prompt as required, mode's enumerated values with meanings, duration_sec allowed values, and tail_image_url's conditional use. It even lists specific recamera template IDs, going far beyond the bare schema.
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 opens with '图生视频：将静态图片转换为动态视频', which clearly states the tool's purpose: converting a static image into a dynamic video. This is a specific verb+resource combination that distinguishes it from sibling tools like generate_video (likely text-to-video) and image_to_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description details the various modes and their prerequisites (e.g., first_tail requires tail_image_url, recamera requires template_id), giving implicit usage context. However, it never explicitly states when to choose this tool over alternatives like generate_video or image_to_image, lacking a direct comparison or exclusion note.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses a notable behavioral trait: '输出视频约需 3～5 分钟（10 秒视频约需 180 秒）' (output video takes approximately 3–5 minutes), indicating this is a long-running operation. It also specifies input format and size limits, which inform the agent of constraints. However, it does not mention side effects, authentication, or whether the operation is asynchronous, though output schema likely covers return details.
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 and front-loaded with the purpose, followed by model name, parameter details in a bullet-like format, and latency estimate. Each sentence adds value with no redundancy. It is appropriately sized for a moderately complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (video generation with two inputs), the description covers purpose, inputs, constraints, and expected latency. It does not explicitly mention prerequisites like whether the image must contain a person's face or the video must contain human motion, but these are implied by '目标人物图片' and '动作参考视频'. Output schema likely handles return format, so this description is sufficient for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must fully explain parameters. It does: 'image_url: 目标人物图片 URL（JPEG/PNG，480×480 以上，1920×1080 以内）' and 'video_url: 动作参考视频 URL（MP4/MOV/WEBM，最长 30 秒）' provide semantics, formats, and constraints. This fully compensates for the bare schema.
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's function: '动作模仿 2.0：提取参考视频中的动作/表情/口型，迁移到目标人物图片上' (extract actions/expressions/lip sync from a reference video and transfer to a target person image). This specific verb+resource pairing distinguishes it from siblings like image_to_video or generate_digital_human by focusing on motion transfer from a video to a person image. It also names the model, reinforcing the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: the tool is used when you have a reference video and a target person image, and it lists the required inputs (image_url, video_url) with constraints (formats, sizes, max duration). It does not explicitly list when to avoid using it or name alternatives, but the usage scenario is evident from the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses the underlying model, input constraints, and a key side effect (higher detail may deviate from the original). It lacks info on postconditions like whether the original is modified, but the output schema covers return format.
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 and front-loaded with the main purpose, followed by a succinct parameter breakdown. No fluff or redundant 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?
The description covers all three parameters with defaults and constraints, includes behavioral context, and benefits from an output schema. It is complete for a straightforward tool, despite not explicitly naming alternatives.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fully compensates by explaining each parameter's type, default, allowed values, and constraints (e.g., image_url format/size, resolution choices, detail range and trade-off).
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's function: enlarging and enhancing image detail to 4K/8K resolution. The specific model name and parameter list further clarify its role, distinguishing it from sibling generation tools like generate_image or image_to_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (whenever image upscaling to 4K/8K is needed). It does not explicitly compare to alternatives, but the purpose is unambiguous enough for an agent to select this tool correctly.
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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