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saram-io

muse-glimmer-agent

by saram-io
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Server Quality Checklist

50%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or misselection. The tool 'ask_agent' has a clearly distinct purpose as the sole entry point for questioning the agent.

    Naming Consistency5/5

    The single tool name 'ask_agent' follows a consistent verb_noun pattern, aligned with its singular function. Naming is clear and predictable.

    Tool Count3/5

    The server has only one tool, which feels thin but is appropriate for its narrow purpose of asking a question. It is not an extreme mismatch, but it borders on insufficient for broader agent workflows.

    Completeness5/5

    The domain is defined as asking the Muse Glimmer agent a question, and the single tool fully covers this operation. There are no obvious gaps within the stated scope.

  • Average 2.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 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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  • 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 carries the full burden of behavior. It states 'ask a question,' which implies a query or read operation, but it does not disclose any side effects, response format, permissions, or limitations. This is a significant gap for a tool with no annotation support.

    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, short sentence with no wasted words. The verb is front-loaded and the resource is clear. It is appropriately concise for a simple tool, though this brevity comes at the cost of 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 an output schema (not shown), so return values may be covered, but the description lacks any context about the nature of questions, expected inputs, or edge cases. An agent has no information about what kinds of prompts are valid or what the response will look like, making it incomplete for a one-parameter tool with no annotations.

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

    Parameters2/5

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

    The schema has 0% description coverage, and the description does not mention the 'prompt' parameter at all. It adds no information about the expected format, content, or purpose of the prompt beyond what the parameter name itself implies. The description fails to compensate for the schema coverage gap.

    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 action (ask) and the resource (Muse Glimmer AI agent). It is specific enough for a tool with no siblings, though it is generic and could be more detailed about the scope of questions. The verb+resource is clear and not tautological.

    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 explicit guidance on when to use this tool versus alternatives, and there are no sibling tools. The description does not mention any prerequisites, context, or conditions for use, leaving the agent to infer that it should be used when a question needs answering.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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