Skip to main content
Glama
thundersoft-td

speech-mcp-server

by thundersoft-td
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, tts and asr, have completely distinct purposes: one synthesizes speech from text, the other converts audio to text. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names are lowercase three-letter acronyms (tts, asr), following a consistent and predictable pattern. The naming style is uniform across the set.

    Tool Count3/5

    With only 2 tools, this is on the thin side. However, the two tools cover the essential capabilities of a speech server (synthesis and recognition), making the count borderline appropriate.

    Completeness4/5

    The tool set covers the two core directions of speech processing (text-to-speech and automatic speech recognition), representing solid coverage. Minor gaps such as voice selection or streaming are not included, but the fundamental workflows are fully supported.

  • Average 3.6/5 across 2 of 2 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 present, so the description carries the full burden. It only states the core conversion without disclosing supported audio formats, language restrictions, output structure, or any failure/limit behavior. This is minimal disclosure for a tool with no structured safety hints.

    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 and front-loaded, containing only essential information in eight words. It avoids redundancy and the title is null, so there is no wasted content.

    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?

    The tool is low-complexity with one parameter and no output schema, and the description plus schema are adequate for basic selection and invocation. However, there are notable gaps: no mention of accepted audio formats, response format beyond 'text', or any operational details, making it merely adequate.

    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 documents the single 'content' parameter as 'url or absolute path of the audio file to transcribe' with 100% coverage. The description adds no extra semantic detail beyond what the schema already provides, so the baseline score of 3 applies.

    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 'Converts audio to text' clearly identifies the tool's function and resource, and the name 'asr' expands to Automatic Speech Recognition. It distinguishes from the sibling 'tts' by being the speech-to-text direction, though it does not explicitly name the alternative.

    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 when transcription is needed, but it provides no explicit when-to-use or when-not-to-use guidance and does not mention the sibling 'tts' as an alternative. This is acceptable but not clearly stated.

    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 discloses that the tool returns a file path, a key behavioral detail. However, it does not mention side effects like file creation location, persistence, or prerequisites, leaving some ambiguity.

    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 concise and front-loaded: two short sentences that deliver the core purpose and return value. No wasted words or repetition.

    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?

    The tool is simple with 3 parameters fully described in the schema, and the description states the return value. It is sufficient for a basic TTS tool, though it could benefit from noting the nature of the returned path (e.g., absolute vs. relative, temporary).

    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 for all three parameters ('text', 'speed', 'encoding') with descriptions. The tool description adds no extra parameter semantics, so the baseline of 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 function: 'Synthesizes text into audio' and specifies the return value ('Return the path of audio file'). This distinguishes it from the sibling tool 'asr' (speech recognition) by explicitly framing it as text-to-speech.

    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 it does not explicitly mention when to use this tool versus alternatives such as 'asr' or provide any exclusions or conditions. The context is clear but lacks explicit guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-server-speech MCP server

Copy to your README.md:

Score Badge

mcp-server-speech MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/thundersoft-td/mcp-server-speech'

If you have feedback or need assistance with the MCP directory API, please join our Discord server