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OnyxFalcon

Piper TTS MCP Server

by OnyxFalcon
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single 'speak' tool has a clearly defined and distinct purpose of converting text to speech.

    Naming Consistency5/5

    The single tool name 'speak' follows a clear verb-based pattern that directly describes its function. With only one tool, consistency is inherently perfect as there are no other tools to compare against.

    Tool Count2/5

    A single tool feels thin for a TTS server that could reasonably support additional functionality like listing available voices, checking synthesis status, or managing audio output. While the core functionality is present, the tool surface is minimal.

    Completeness2/5

    The server provides basic text-to-speech conversion but lacks complementary tools that would create a complete TTS workflow. There are no tools for voice management, synthesis monitoring, or audio file handling, leaving significant gaps in the domain coverage.

  • Average 4.1/5 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

  • 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 discloses that the tool plays speech through speakers and returns a success or error message, which covers basic behavior. However, it lacks details on potential side effects (e.g., audio output interruption), permissions, or error handling, leaving gaps in transparency.

    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 well-structured and front-loaded with the core purpose, followed by a clear breakdown of parameters and returns. Every sentence adds value without redundancy, making it efficient and easy to parse for an agent.

    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 complexity (6 parameters) and no annotations, the description does a good job covering parameters and basic behavior. With an output schema present, it doesn't need to detail return values. However, it could improve by addressing potential constraints like audio device requirements or usage limits.

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

    Parameters5/5

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

    The description adds significant meaning beyond the input schema, which has 0% coverage. It explains each parameter's purpose (e.g., 'speaker_id: Voice speaker ID', 'length_scale: Speech speed control'), including default values and effects (e.g., 'lower = faster'), fully compensating for the schema's lack of descriptions.

    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 with a specific verb ('Convert text to speech and play it through the speakers'), identifying both the action and resource. It distinguishes itself by specifying the exact functionality without ambiguity, and since there are no sibling tools, no differentiation is needed.

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

    Usage Guidelines3/5

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

    The description implies usage for text-to-speech conversion but provides no explicit guidance on when to use this tool versus alternatives. With no sibling tools mentioned, there's no context for comparison, leaving the agent to infer usage based on the stated purpose alone.

    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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