seedream-mcp

Server Quality Checklist

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Latest release: v0.1.9
Disambiguation4/5
The creation tools (text_to_image, edit_image, decompose_layers) are distinct in purpose, though all share similar descriptions. get_task, login, and check_pricing are clearly separate, but the three creation tools could be confused if the descriptions were less clear about their specific operations.
Naming Consistency4/5
Tool names use a consistent snake_case verb_noun pattern (e.g., 'get_task', 'edit_image', 'check_pricing'). The only minor deviation is 'login' (a single verb) and 'decompose_layers' (verb with object but not a standard CRUD noun). Overall, the pattern is predictable.
Tool Count4/5
With 6 tools, the count is well within the ideal range for a focused server. Each tool covers authentication, task creation, status checking, and pricing, which is reasonable for the scope. No redundancy or bloat is apparent.
Completeness3/5
The server covers the core lifecycle of creating tasks, checking results, and authenticating, but it lacks a cancellation or listing operation. Users cannot list all tasks or cancel a task, which could be a gap for an agent managing multiple tasks. However, for simple workflows, the core is sufficient.
Average 3.3/5 across 6 of 6 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on August 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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?
No annotations provided, so the description must disclose behavioral traits. It mentions returns (task id, status, output URLs) but does not explain async behavior, polling, timeouts, or safety checks. The 'wait' parameter implies polling but is not described.
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 is a single sentence that front-loads the key action. It is concise, though it could benefit from additional context without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 14 parameters, no output schema, and no annotations, the description is severely incomplete. It omits essential context like parameter purpose, error handling, and task lifecycle.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 14% (2 of 14 parameters have descriptions). The tool description adds no parameter information, failing to compensate for the low schema coverage.
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 creates a Seedream task for editing images and returns task id, status, and output URLs. It directly distinguishes from siblings: login, get_task, and check_pricing.
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 on when to use this tool versus alternatives. The description only states what it does, lacking context for selection or exclusion.
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, the description must disclose behavior but only states it returns a task id, status, and output URLs. It does not reveal that the operation is likely asynchronous, that status may require polling, that image_url has format/size constraints, or any error/rate-limit behaviors. Key behavioral aspects are omitted.
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 is concise and well-structured in a single sentence, avoiding verbosity. However, it is too sparse to cover essential context, but for what it says, it is clean and understandable.
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?
The tool has no annotations, and the description only provides a high-level summary. It omits important context such as asynchronous behavior, input constraints beyond that in the schema, output details, and relationship to other tools. Significant gaps remain for an agent to reliably use the tool.
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 schema provides descriptions for 5 of 9 parameters (56% coverage), including essential ones like image_url, wait, model, prompt, and output_format. However, the description itself adds no parameter-level detailahan selain that it returns a task id, status, and output URLs. Parameters like size, timeout_ms, callback_url, and poll_interval_ms are left undocumented in both schema and description, leaving gaps.
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 action (creating a Seedream task) and the resource (RunAPI for decompose layers), but does not elaborate on what decomposition entails or distinguish it deeply from sibling image editing tools. It is more specific than a tautology but lacks detailed purpose context.
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 on when to use this tool vs. alternatives like edit_image or get_task. The description only says 'Create a Seedream task' without mentioning prerequisites, use cases, or exclusions. The schema hints at asynchronous behavior via wait/poll parameters, but the description itself offers no usage direction.
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 behavioral disclosure. It only mentions the return values (task id, status, output URLs) but does not disclose that the task is asynchronous, whether it waits, or any other traits like safety checks or rate limits. This is a significant gap given the schema includes wait and polling parameters.
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 two sentences, front-loaded with the core action, and contains no filler. Every word earns its place, making it highly concise and well-structured.
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?
The tool has 13 parameters, no output schema, and no annotations, making complete context essential. The description provides only the basic purpose and return type, omitting important usage context such as required parameters, async behavior, and parameter semantics, so it is far from complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only 15% schema description coverage, the description adds no parameter-level details. It does not explain what 'seed', 'output_format', or other parameters do, and fails to compensate for the low schema coverage.
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 uses the verb 'Create' and specifies the resource 'Seedream task on RunAPI' with the scope 'text to image', clearly distinguishing it from sibling tools like edit_image. It also states the return value, further clarifying the tool's function.
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 the tool is for generating a text-to-image task, giving clear context for when to use it. However, it does not explicitly state exclusions or recommend alternative tools such as get_task or edit_image for other cases, leaving usage guidance mostly implicit.
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, the description carries the full burden. It states a lookup operation but does not disclose side effects, permissions, return value format, or error behavior. Minimal transparency is provided.
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 a single concise sentence that immediately states the action and scope. It is well-structured and free of unnecessary information.
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 description is adequate for a simple lookup tool, but it does not mention the output format or any prerequisites (e.g., authentication). Given no output schema, the description could be more complete about what is returned.
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 schema already provides descriptions for both parameters, and the tool description adds no additional meaning. Since schema coverage is 100%, the baseline score 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?
The description clearly states the tool's purpose: to look up RunAPI pricing for the seedream model line. It specifies the resource (pricing) and the scope (seedream), making it distinct from sibling tools like edit_image or 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?
The description implies usage when pricing information is needed but provides no explicit when-to-use or when-not-to-use guidance, nor does it compare with alternatives. It is adequate but not explicit.
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 must disclose all behavioral traits. It mentions the browser login flow and file saving, but omits details like side effects (e.g., overwriting existing config), error handling, or that it requires user interaction. This leaves ambiguity for an AI agent.
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?
One sentence with no wasted words. It starts with the verb 'Authenticate' and efficiently conveys the core action and destination. Ideal conciseness for a simple 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's simplicity (1 optional parameter, no output schema), the description covers the essential flow and output location. Minor missing context: it doesn't state that this is a setup step or that it requires a browser, but overall adequate for the 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 coverage is 100% (one parameter fully described). The tool description does not add meaning beyond the schema; the force parameter is explained in the schema itself. Baseline 3 is appropriate as the description adds no extra semantic value.
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: authenticate via browser PKCE login flow and save the API key to a specific config file. It uniquely identifies the tool's purpose and distinguishes it from unrelated sibling tools like check_pricing or 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?
The description implies when to use (to authenticate), but does not provide explicit guidance on when not to use or alternatives. The force parameter hint is in the schema, not the description. No exclusions or context about prerequisites.
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?
Annotations are none, so the description must carry full behavioral disclosure. It states the tool fetches status and result payload, indicating a read-only nature. However, it does not mention whether the call is blocking, rate-limited, or what happens if the task is still running. It adds some value by describing the output (status and latest result) but lacks depth.
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 a single concise sentence that conveys the core action and object. No unnecessary words, front-loaded with the verb 'Fetch'. Perfectly structured for a simple 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?
For a simple read operation with 2 fully documented parametersches, the description is sufficient. It states what is fetched (status, result payload) without needing to explain return values since no output schema is present. It does not mention asynchronous behavior or poll semantics, but that is not critical for a basic status fetch.
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% — both 'task_id' and 'action' have descriptions and action has an enum. The description does not add parameter-specific meaning beyond the schema, but that's acceptable given the high coverage. Baseline 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?
The description clearly states the action: 'Fetch the current status and latest result payload for a seedream task.' It identifies the resource (task) and the specific data retrieved. This distinguishes it from sibling tools that create tasks (decompose_layers, edit_image, text_to_image) by focusing on retrieval rather than submission.
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 after a task has been created (since it fetches status/result of a 'seedream task'), but it does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites or that this should be polled after async operations. The context is clear but not explicit.
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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