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Server Quality Checklist

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  • Latest release: v2.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct function: analyze_image performs AI analysis, upload_to_supabase handles storage, and get_security_status provides configuration/audit info. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (analyze_image, upload_to_supabase, get_security_status) using snake_case. The pattern is predictable and uniform across the set.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped for its purpose of AI image analysis and upload, which is appropriate. Each tool serves a distinct and necessary function, and the count is well within the ideal range.

    Completeness4/5

    The core workflow is covered: analyze an image, upload results, and retrieve security status. Minor gaps exist, such as no way to list or delete uploaded files, but these are not essential to the server's primary purpose and can be worked around.

  • Average 3.1/5 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 status not available
  • 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

  • Behavior2/5

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

    No annotations are provided, so the description must carry the full burden. It mentions 'security validation' as a behavior but does not disclose what validation entails, whether the operation is idempotent, what permissions are required, or what side effects occur. This is a thin disclosure for a mutation tool.

    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 sentence that is concise and front-loaded with the action. Every word adds value, stating the target (Supabase Storage) and a notable feature (security validation). There is no redundant information.

    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 is complex with 6 parameters, 5 required, nested objects, and no output schema or annotations. The description provides no information about return values, error behavior, validation specifics, or usage context. It is inadequate for an agent to fully understand the tool's behavior and edge cases.

    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?

    With 100% schema description coverage, the baseline is 3. The description adds little beyond the schema, though 'analysis results' might loosely map to the metadata parameter. It does not clarify parameter syntax or relationships, but the schema already documents all fields thoroughly.

    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 uploads images or analysis results to Supabase Storage, which is a specific verb and resource. While it doesn't explicitly differentiate from siblings (analyze_image, get_security_status), the upload action is unambiguously distinct from analysis or status retrieval.

    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. There is no mention of prerequisites, exclusions, or comparison with sibling tools. The intended use case is only implied by the action of uploading.

    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 safety and behavior, but it only vaguely mentions 'security validations' and 'securely'. It doesn't state whether the operation is read-only, what happens on validation failure, or how it handles remote URLs vs local paths. The behavioral traits are under-specified.

    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, compact sentence that front-loads the main action. There is mild redundancy between 'Securely' and 'security validations', but no wasted words or irrelevant detail.

    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 a oneOf constraint (image_path or image_url) and an optional analysis_type, but the description doesn't explain these constraints or the return value. Without an output schema or annotations, the description should provide more behavioral and usage context, but it remains minimal.

    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 has 100% parameter description coverage, providing clear meanings for image_path, image_url, and analysis_type. The description adds only a hint about 'automatic type detection' which relates to analysis_type but is already implied by the schema's optional enum. No additional parameter-level context is provided.

    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 a specific verb ('analyze') with a clear resource ('an image'), and adds a method ('using Google Gemini AI') and scope ('automatic type detection'). This distinguishes it from sibling tools like upload_to_supabase and get_security_status.

    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 doesn't mention sibling tools, prerequisites, or exclusions. The context is only implied by the action described, 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?

    Annotations are entirely absent, so the description must carry the full burden. It implies a read-only operation via the verb 'Get,' but does not clarify whether any authentication is required, if the data is sensitive, or how the audit information is structured. This gap is significant for an agent deciding whether this tool is safe to invoke.

    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 short sentence, front-loaded with the action and resource. It wastes no 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?

    For a zero-parameter read operation, the description is adequate but lacks detail about the return format or content structure. Since there is no output schema, the description should do more to explain what the agent can expect from the response.

    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 accepts no parameters (empty schema), so the description has nothing to add beyond the schema. With 0 parameters, the baseline of 4 is appropriate.

    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 uses the specific verb 'Get' and identifies a concrete resource: 'security configuration and audit information.' This clearly conveys the tool's function. Sibling tools (analyze_image, upload_to_supabase) are unrelated domains, so no explicit differentiation is needed, but the description could specify the scope further.

    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, nor any prerequisites or exclusions. There is no mention of appropriate contexts, making it purely a bare statement of function.

    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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