z_ai_image_gen_mcp

Server Quality Checklist

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Latest release: v2.0.0
Disambiguation4/5
Tools are mostly distinct: image vs video generation, sync vs async, and combined download operations. However, there is some overlap between generate_image_async+get_async_result and generate_image (sync), but descriptions clarify usage.
Naming Consistency4/5
Names follow a mostly consistent verb_noun pattern (e.g., list_models, generate_image). A minor inconsistency: 'get_async_result' is ambiguous (could be image or video), but the complementary 'get_video_result' helps. Compound names like 'generate_and_download_image' break the pattern slightly.
Tool Count5/5
10 tools cover the core workflows for image and video generation without being excessive. Each tool has a clear purpose, and the number is well-scoped for the domain.
Completeness4/5
Covers essential operations: model listing, generation (sync/async), result retrieval, and download. Lacks a synchronous video generation option and management tools (e.g., list/cancel tasks), but the main generation pipeline is complete.
Average 3.8/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses synchronous execution and direct URL return, which are key behavioral traits. However, with no annotations, the description carries the full burden and omits other relevant details such as authentication, rate limits, or cost implications.
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?
Two sentences, highly concise and front-loaded with the core purpose. Every sentence adds value: first states the action and output, second recommends usage. No wasted words.
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?
Given 5 parameters and no output schema, the description is overly minimal. It covers the basic purpose but lacks details on error handling, parameter interdependencies, or example usage, leaving the agent with incomplete contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for all 5 parameters, so baseline is 3. The description does not add extra semantics beyond what the schema already provides for parameters like prompt, size, model, quality, and user_id.
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?
Description clearly states 'Generate an image synchronously from a text prompt' and 'Returns the image URL directly', which specifies the verb and resource. It implicitly distinguishes from the sibling generate_image_async by noting synchronous behavior, though it does not explicitly name the async alternative.
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?
Says 'Use this for most image generation tasks' to indicate when to use, but provides no guidance on when not to use or what alternatives exist (e.g., generate_image_async). No exclusions or context with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states 'Retrieve the result' without disclosing whether the call is idempotent, non-destructive, or if it can return pending states. Lacks details on polling 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?
Two sentences with no redundancy; front-loaded with the core purpose. Every word is meaningful.
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?
Given a single parameter and no output schema, the description is minimally adequate. However, it omits mention of potential states (e.g., pending, completed) and any guidance on polling behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes the parameter as 'The task ID returned by generate_video'. The description adds no additional meaning beyond what is in the 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?
Description clearly states 'Retrieve the result of an asynchronous video generation task', specifying the action and resource. Distinguishes from siblings like get_async_result by explicitly mentioning video generation.
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 phrase 'Use the task ID from generate_video' gives context for when to use, but there is no guidance on when not to use, such as for tasks still processing, or alternatives like get_async_result.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of behavioral disclosure. It does not mention whether the call is read-only, what happens if the task is pending or fails, any authentication requirements, or rate limits. Critical behavioral traits are missing.
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 extremely concise: a single sentence with two clear parts. It is front-loaded with the purpose and immediately provides usage context. No words are wasted.
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?
Given the tool's simplicity and lack of output schema, the description is somewhat incomplete. It omits details about the result format, possible statuses (e.g., pending, success, failure), and error handling. For a polling tool, such context is important for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers the parameter 'task_id' with a clear description, achieving 100% coverage. The tool description adds minimal new meaning, only reinforcing that the ID comes from generate_image_async. Thus, the description does not significantly enhance the schema's explanation.
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 action ('Retrieve the result') and the resource ('asynchronous image generation task'), and it explicitly links to the sibling tool 'generate_image_async', differentiating it from synchronous or video counterparts.
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 specifies when to use this tool: after calling generate_image_async and using its task ID. It provides clear context but does not explicitly state when not to use it or list alternative tools for different scenarios.
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 carries full burden. It discloses that it lists models and capabilities, which implies a safe read operation. However, it does not describe specifics such as whether the list is real-time or cached, auth requirements, or rate limits.
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?
One sentence that is efficient and front-loaded. The word 'capabilities' is slightly vague but acceptable. Could be more specific without losing conciseness.
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?
With no output schema and no annotations, the description is minimally complete. It tells the agent it lists models and capabilities, but not the return format or whether capabilities are structured. Adequate for a simple list tool but could add more context.
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 has zero parameters, so schema coverage is 100%. The description adds meaning by specifying that the output includes 'capabilities', which is beyond the schema. No param details needed.
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 action ('List') and the resource ('available Z.AI video generation models') with an added detail about capabilities, distinguishing it from the sibling 'list_models' tool.
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?
No guidance is provided on when to use this tool versus alternatives like 'list_models'. The agent is left to infer that this is specific to video models, but no explicit when-to-use or when-not-to-use advice is given.
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 carries full burden. It mentions the combined operation and output formats but does not explain polling behavior, potential waiting, or side effects. Parameters like max_wait and poll_interval hint at async behavior, but the description lacks explicit disclosure.
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 three sentences with no redundancy. It front-loads the core action, then clarifies the combination, and ends with output format. Every sentence is necessary and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 9 parameters and no output schema, the description is too minimal. It omits details about the async/polling behavior, how to use the output format, and the requirement for file_output path when output is 'file_output'. The presence of sibling tools like generate_image_async and get_async_result further highlights the need for clearer behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions. Each parameter is adequately described in the schema, so no further elaboration is needed.
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 and downloads an image in a single operation, combining generate_image and download_image. It distinguishes itself from sibling tools by explicitly naming the combination and indicating it's best for immediate image data needs.
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 a clear usage context ('Best for when you want the image data immediately'). It does not specify when to avoid using it or when to prefer the separate sibling tools, but the combined nature implies a convenience trade-off.
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?
Discloses async behavior and model differences but omits details on failure modes, timeouts, auth requirements, or rate limits. No annotations exist to compensate, so description carries full burden but falls short.
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?
Two sentences, no redundancy. Front-loaded with the core action and key details. Every word earns its place.
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?
Adequate for a tool with thorough schema descriptions, but lacks examples or guidance on parameter combinations. No output schema means description should clarify return value further, which it does minimally.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema itself documents all parameters. The description adds minimal extra context beyond listing model capabilities, so baseline of 3 is appropriate.
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?
Clearly states it generates a video asynchronously from text or images and returns a task ID. Lists supported models, distinguishing it from sibling tools like generate_image_async and get_async_result.
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?
Indicates async nature and need to poll for results, hinting at use with get_async_result. Does not explicitly state when not to use or compare with synchronous alternatives like generate_and_download_video.
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?
No annotations are provided, so the description carries the full burden. It explains the core behavior (download, return base64 or save to file) and mentions URL expiry. However, it lacks details on potential failure modes, rate limits, or authentication requirements. With no annotations, this is minimally adequate but not comprehensive.
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 extremely concise: two sentences and a note. Every sentence adds value: first states action and options, second provides usage context, and the note gives an important constraint. No wasted words.
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 simple download tool with three parameters and no output schema, the description covers the essential points: what it does, when to use it, output options, and a critical caveat (URL expiry). It lacks details about error handling or file path requirements, but these are partly covered by the schema. Overall, it is quite complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all three parameters have descriptions). Baseline is 3. The description adds context by echoing the output enum and clarifying the URL source from generation, but does not significantly deepen parameter understanding beyond the 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 verb 'Download' and the resource 'image from a URL', and specifies the available output options (base64 or file). It distinguishes from sibling tools by indicating this is for downloading an image URL, typically after generation.
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 explicitly says 'Use this after generating an image to get the actual image data', providing clear when-to-use guidance. It also notes URL expiry, which is a time constraint. It does not explicitly contrast with sibling tools that combine generation and download, but the context is sufficient.
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?
No annotations provided, so description carries full burden. It describes a read-only listing but does not disclose any behavioral traits like rate limits or authentication requirements. Lack of side effects makes this adequate, but not exceptional.
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?
Single sentence, front-loaded with the primary action, no unnecessary words. Efficient and clear.
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?
No output schema, but for a list tool with no parameters, the description is reasonably complete. Could specify output format but not essential given sibling context (list_video_models).
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?
No parameters exist, so description cannot add parameter info. With 0 params, the description is sufficient and clear.
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?
Description states the verb 'List', the resource 'available Z.AI image generation models', and what it provides ('their capabilities'). It clearly distinguishes from sibling tools like generate_image and list_video_models.
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?
No explicit guidance on when to use or when not. However, the context of sibling tools (e.g., list_video_models for video models) implies usage for image model discovery.
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?
The description mentions polling behavior and returning the file, adding value beyond the schema. No annotations exist, so this is adequate disclosure.
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?
Two sentences with clear, front-loaded purpose. No redundant or empty phrasing.
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?
With 15 parameters, no output schema, and no annotations, the description is too brief to fully guide the agent on handling failures, return format, or usage nuances.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no parameter-specific information beyond the 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 verb 'generate and download' and the resource 'video', and distinguishes from sibling tools by indicating it is for immediate retrieval.
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?
'Best for when you want the video data immediately' provides context for when to use, but does not explicitly state when not to use or name alternatives like generate_video/get_video_result.
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 discloses the async behavior and the need to poll for results. It could add details about polling mechanics or error handling, but it is sufficient for most use cases.
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?
Two sentences: first defines the tool's action, second provides usage guidance. No fluff, front-loaded information.
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 no output schema, the description adequately mentions the return value (task ID). It could explicitly reference 'get_async_result' for polling, but the context of async generation is clearly set.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already explains all parameters. The description does not add additional meaning to individual parameters; it sets the overall context for the async task.
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 it starts an asynchronous image generation task and returns a task ID. It uses specific verbs ('Start', 'Returns') and resource ('task ID'). The async nature distinguishes it from siblings like 'generate_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?
Explicitly says 'Use this for long-running generations or when you need to process multiple images.' This provides clear context for when to use, though it does not explicitly mention alternatives or when not to use.
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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