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alecslacker

byteplus-image-mcp

by alecslacker
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    byteplus_generate_image is solely responsible for creating images, while byteplus_list_models handles model discovery and quota information. There is no functional overlap between the two tools.

    Naming Consistency5/5

    Both tool names follow the same byteplus_verb_noun pattern: generate_image and list_models. The naming is consistent, predictable, and clearly conveys each tool's action.

    Tool Count4/5

    With only two tools, the server is slightly under the typical 3-15 range, but the count is reasonable for a narrow image-generation purpose. Each tool earns its place: generation is the core action, and model listing supports informed model selection.

    Completeness5/5

    For the server's stated purpose of text-to-image generation, the surface is complete: list_models lets an agent choose the right model, and generate_image produces the image with appropriate parameters. There are no obvious dead ends in the core workflow.

  • Average 4.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior5/5

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

    The description discloses meaningful behavioral traits beyond the annotations: quota consumption ('mengurangi kuota gratis akun'), URL validity ('valid 24 jam'), local download behavior via save_to_disk, watermark default, and output JSON structure. Annotations only flag readOnly=false/idempotent=false/destructive=false, so this behavioral detail is additive and non-ctradictory.

    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 long but well-structured with clear sections: purpose, Args, Returns, success example, and usage triggers. The most important scoping information is front-loaded, and each section adds actionable value rather than repeating boilerplate.

    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 an external API tool with quota implications, multiple models, download behavior, and a JSON return format, the description is effectively complete. It includes an output example, parameter semantics, default behavior, and use-case routing; the only minor omission is sekuence_format, which is already covered in the schema.

    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?

    Despite the reported low top-level schema coverage, the Args block documents the main parameters with defaults, constraints, and purpose-to-model mapping, which adds practical meaning beyond the raw schema. It omits sekuence_format from the prose, but the schema already defines that parameter fully, so the gap is minor.

    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 leads with a concrete verb and resource: 'Generate gambar via BytePlus ModelArk (Seedream)' and enumerates concrete use cases: logo, mockup UI/UX, and dokumen ilustrasi. This makes the tool's purpose unambiguous and clearly distinguishes it from the sibling 'byteplus_list_models'.

    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 'Kapan dipakai' section provides explicit usage triggers with purpose mapping ('Buatkan logo' -> purpose='logo', etc.), which is clear practical guidance. It does not explicitly mention when not to use the tool or compare it to alternatives, so it stops short of the highest bar.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavior beyond annotations by specifying the return format: a JSON string list with model_id, use_case, price per image, and free quota remaining. No contradictions with annotations.

    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 precise return value breakdown and a usage directive. Every sentence contributes useful information with no filler.

    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 zero-parameter read-only listing tool, the description fully explains what it returns, why the agent should call it, and when to use it relative to the sibling. Annotations cover side-effect safety, and the return structure is described in sufficient detail.

    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 tool has zero parameters, so parameter semantics are trivially satisfied. The description correctly focuses on output and usage rather than inventing parameter detail. Baseline 4 for no-parameter tools 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 identifies the tool as listing available Seedream models with selection guidance and quota info. It distinguishes itself from the sibling tool by naming byteplus_generate_image and positioning this as the pre-selection step.

    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 explicitly instructs to use this tool before byteplus_generate_image when uncertain about model choice. This provides clear when-to-use guidance and directly routes the agent to the correct tool among the siblings.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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