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sonnyflylock

AI Directory MCP Server

by sonnyflylock
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JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: chatting with an AI, filtering services by country, getting details by ID, and listing all services. No overlap or confusion.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (chat_with_ai, find_ai_services_by_country, get_ai_service, list_ai_services), making the set predictable and easy to navigate.

    Tool Count5/5

    With 4 tools, the server is well-scoped for an AI directory: listing, detail, filtered search, and interaction. The count feels natural and not excessive.

    Completeness4/5

    The tool set covers core directory operations (list, get, search) and interaction (chat). Minor gaps exist (e.g., no filtering by provider type or category), but the surface is largely complete for the domain.

  • Average 3.8/5 across 4 of 4 tools scored. Lowest: 3.1/5.

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

    • 0 of 1 community issues answered or closed 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

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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 bears full responsibility. It only states the tool finds AI services by country but does not disclose behavioral traits such as whether partial matching is allowed (the schema does mention it), return format, read-only nature, or any limitations.

    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 short sentence, which is concise and front-loaded. However, it could be expanded slightly to include key behavioral context without losing conciseness. Still, it is not bloated.

    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's simplicity (one parameter, no output schema, no annotations), the description is too minimal. It does not explain what the tool returns (e.g., 'returns a list of AI service names and details'), how it handles partial matching, or any edge cases. More context is needed for an agent to use it correctly.

    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% with a clear description of the 'country' parameter including examples. The tool description adds no new semantic value beyond the schema; it simply restates the parameter's purpose. 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 tool's purpose: 'Find AI services available in a specific country or region'. It uses a specific verb and resource, and the tool name effectively distinguishes it from siblings like 'list_ai_services' (which likely lists all services) and 'get_ai_service' (singular).

    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 like 'list_ai_services'. It does not mention prerequisites, exclusions, or preferred scenarios, leaving the agent to rely solely on the tool name for context.

    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. Description describes a read-only action (getting a URL) and does not mention auth, rate limits, or side effects. Adequate but minimal.

    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 key action. Every word earns its place.

    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 single-parameter tool without output schema, the description covers purpose and recommended usage. Could add expiry or usage details of the URL, but not essential.

    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% with detailed enum explanations. Tool description adds no additional parameter context beyond the schema, so baseline 3 applies.

    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 'Get', resource 'webchat URL', and purpose 'chat with a Voxie AI persona'. It distinguishes from siblings which are about managing AI services, not chatting.

    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?

    States 'recommended way to interact with Voxie AIs', implying primary use case. Does not explicitly exclude alternatives or provide when-not-to-use guidance, but sibling tools provide enough context.

    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 adds behavioral context about access methods (webchat and phone for some), but does not disclose other traits like pagination, ordering, or rate limits. For a simple list tool, this is adequate but not exhaustive.

    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 is front-loaded with the core functionality and efficiently covers purpose and optional filter.

    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 list tool with one optional parameter and no output schema, the description is largely complete. It explains the filter options and access methods. It could mention absence of pagination or ordering details, but overall it meets the needs.

    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% coverage with a detailed description of the provider parameter. The tool description mentions filtering but does not add value beyond the schema's parameter description. 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 verb 'List' and resource 'AI services', adds context about access via webchat (and phone for some), and effectively distinguishes from siblings like chat_with_ai, find_ai_services_by_country, and get_ai_service.

    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 indicates usage for listing all services or filtering by provider type, providing clear context. However, it lacks explicit guidance on when not to use this tool or direct comparison with sibling tools, though the distinction is implicit.

    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 disclose behavioral traits. It states 'get details' but does not explain what the details include, return format, or any side effects. For a simple read operation, minimal behavior is implied, but more clarity on the response structure would be beneficial.

    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, front-loaded sentence with no extra words. Every word is essential and informative.

    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 (one parameter, no output schema, no annotations), the description is mostly complete. It identifies the resource and lookup method, though it could mention the expected return type or error scenarios.

    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 coverage is 100%, with the parameter described as 'The service ID'. The description adds concrete examples of valid IDs, providing added meaning 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 uses 'Get details about a specific AI service by its ID', specifying the verb, resource, and method. It distinguishes from siblings like list_ai_services (listing all) and find_ai_services_by_country (filtering by country).

    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 implies usage when you have a specific service ID and want details, especially with examples ('voxie-us', 'chatgpt-us'). However, it lacks explicit guidance on when not to use it or comparisons to alternatives.

    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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