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tomekkorbak

voice-status-report-mcp-server

by tomekkorbak
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.3

  • Disambiguation5/5

    Only one tool exists, so there is no risk of confusion between tools.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent.

    Tool Count3/5

    A single tool is thin for a typical MCP server, but it may be appropriate if the server's scope is limited to providing a summary.

    Completeness3/5

    The tool covers the stated purpose of summarizing actions, but lacks additional voice-related functionality that might be expected (e.g., full report creation or audio control).

  • Average 3.9/5 across 1 of 1 tools scored.

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

    • 1 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

  • 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

  • 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 mentions 'send a short audio message' but the input schema only expects a text parameter, creating potential confusion. The description says the message should be one 10-15 word sentence, but does not explain how the audio is generated or any limitations. Lacks details about side effects or format.

    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 two sentences: one defining the action and one specifying usage. It is succinct and front-loaded with purpose. However, it could be slightly restructured to separate the 'what' from the 'when' more clearly.

    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 the lack of annotations, output schema, and parameter descriptions, the description needs to cover all behavioral aspects. It covers purpose and usage well, but omits important details like what happens after sending the message, whether the text length is validated, and the format of the audio output. As a result, the agent may have unanswered questions.

    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 input schema has one required parameter 'text' with no description (0% coverage). The description implies that 'text' should be a concise sentence, but it does not explicitly link the parameter to the audio message content. The mention of 'audio message' in the description is not reflected in the schema, potentially confusing the agent.

    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 action: sending a short audio message summarizing what was done and why. It specifies the exact verb 'summarize' and provides a clear resource. Although no sibling tools exist for comparison, the purpose is unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly states when to use the tool: after every code snippet generation or command execution, and when handing control back to the user. This is highly directive and leaves no ambiguity about usage context.

    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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