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ElevenLabs Image Generation MCP Server

by ddunford
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: generating images, checking session status, and listing models. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case: generate_image, get_session_status, list_models.

    Tool Count5/5

    Three tools is appropriate for a focused image generation server, covering essential actions without being too sparse or bloated.

    Completeness4/5

    The set covers the core image generation workflow (list models, check session, generate) but lacks tools for managing or retrieving past generations, leaving minor gaps.

  • Average 3.9/5 across 3 of 3 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 passing

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  • 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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It correctly implies a read-only operation ('List') but does not disclose potential behavioral traits such as caching, rate limits, or idempotency. For a straightforward list function, this is marginally sufficient.

    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, clear sentence with no unnecessary words. It front-loads the action and resource, earning its place efficiently.

    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 or parameters, the description is largely complete for a list tool. However, it could add minor context about typical use (e.g., to check available models before generating) to enhance completeness. Score 4.

    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 trivially 100% as there are no parameters. The description adds no parameter semantics, which is acceptable since no parameters exist. Score at baseline 3.

    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 'List', the resource 'image generation models', and the scope 'on ElevenLabs'. It directly conveys the tool's function and is easily distinguishable from sibling tools like generate_image and get_session_status.

    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 (listing models before generating images) but does not explicitly provide when or when-not to use, nor does it mention alternatives. For a simple list tool, this is adequate but lacks explicit guidance.

    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 must cover behavioral traits. It mentions checking login and getting session info, but does not specify side effects (likely none), required permissions, or whether the operation is read-only. Adequate but not thorough.

    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, front-loading the action. No unnecessary 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 no output schema and no annotations, the description provides the essential purpose but lacks detail on return format or session information content. Could be more complete for a simple 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 tool has no parameters, and schema coverage is 100% (trivially). Description adds no parameter info, which is acceptable. Baseline for 0 params is 4.

    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 checks login status and gets session info. It distinguishes from siblings (generate_image, list_models) which are unrelated, making the specific verb+resource clear.

    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 use for checking authentication, but does not explicitly state when to use versus alternatives or provide exclusion criteria. No guidance on preconditions 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.

  • Behavior3/5

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

    With no annotations, the description must disclose behavior fully. It mentions file path return and uses ElevenLabs feature, but does not discuss authentication requirements, error handling, or potential side effects. The description is adequate but not detailed.

    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, no wasted words, and directly states the core purpose and output. It is well-structured and front-loaded.

    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 7 parameters (1 required) and no output schema, the description explains the return value (file path) and mentions the ElevenLabs feature. It could add more detail about saving behavior or default directory, but is mostly complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so baseline is 3. The description adds value by stating the tool returns a file path, and it mentions 'using ElevenLabs Image & Video feature', which gives context beyond the schema. Scores 4.

    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 explicitly states 'Generate an image using ElevenLabs Image & Video feature' and mentions the return value 'path to the downloaded image file'. It clearly distinguishes from sibling tools like get_session_status and list_models, which are unrelated.

    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 for image generation but does not provide explicit guidance on when to use versus alternatives, nor does it mention prerequisites or limitations. The sibling tools are unrelated, reducing the need for differentiation, but still no exclusions are given.

    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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