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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'brightsy' has a clear and distinct purpose as a proxy to the Brightsy AI agent.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'brightsy' is straightforward and matches the server's purpose.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it offers minimal functionality and can limit agent capabilities. While it might suffice for a simple proxy, it feels thin and under-scoped for typical MCP server expectations.

    Completeness3/5

    The tool surface is incomplete for a general-purpose AI agent proxy, lacking operations like configuration, status checks, or specific request types. However, the single tool covers the basic proxy function, leaving notable gaps but not entirely failing.

  • Average 2.7/5 across 1 of 1 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
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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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'proxy requests' which implies some form of communication forwarding, but doesn't describe authentication requirements, rate limits, error handling, response format, or what the Brightsy AI agent actually does. This leaves significant behavioral gaps for a proxying 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 extremely concise at just 6 words, with zero wasted language. It's front-loaded with the core purpose and contains no unnecessary elaboration. This is an example of efficient communication that earns its place.

    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?

    For a proxying tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the Brightsy AI agent is, what types of requests are proxied, what authentication is needed, or what format the responses take. The combination of vague purpose and missing behavioral context creates significant gaps.

    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%, so the schema already documents the single 'messages' parameter with its structure. The description adds no additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool 'proxy requests to an Brightsy AI agent', which provides a basic verb+resource combination. However, it's vague about what 'proxy requests' specifically entails - whether it's for chat, API calls, or other interactions. Without sibling tools, differentiation isn't needed, but the purpose lacks specificity about the nature of the proxying.

    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 context about prerequisites or appropriate scenarios. With no sibling tools, the absence of explicit 'when-not-to-use' guidance is less critical, but there's still no usage context provided.

    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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