cursor-tts-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
The two tools have completely distinct purposes: speak outputs audio, stop halts playback. No overlap or ambiguity exists between them.
Naming Consistency5/5Both tool names are single-word imperative verbs (speak, stop) that directly describe their actions. The naming pattern is perfectly consistent and predictable.
Tool Count4/5With only two tools, the set is minimal but appropriate for a narrow TTS status-line server. It feels slightly thin, but each tool earns its place.
Completeness5/5For the stated purpose of speaking short status lines and stopping playback, the tools cover the full lifecycle with no obvious gaps. Stop even offers queue-clearing behavior.
Average 4.5/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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.jsonto 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations to carry safety or behavioral signals, the description does most of the work itself. It discloses that the tool should not be used to read code or long text, that it should be called even when a conclusion is typed, and that spoken output must be short English (≤80 characters). It does not explain interrupt or priority behavior, but the core behavior is well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and usage, and each instruction serves a distinct function. The final 'Do not force Chinese' clause is somewhat redundant after the 'system UI language (en)' note, but it is a cheap and useful reminder.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple output tool, the description covers required inputs, category mapping, output style, language, and call timing. The main gap is the undocumented 'priority' and 'interrupt' parameters, but these are optional and defaulted, so the description remains largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It does add meaning for 'text' (short, English, ≤80 characters) and 'category' (progress, errors, conclusions, decisions), corresponding to the enum values. However, 'priority' and 'interrupt' are never explained, leaving their semantics to names and defaults alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear verb-plus-resource statement ('Speak a short status line for the user') and then enumerates the exact event types it is for: progress, task conclusions, errors/blocks, and decisions. This makes the tool's role obvious and distinguishes it from the sibling tool 'stop'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to call it ('Use for real progress, task conclusions, errors/blocks, and when the user must decide or act') and when not to ('Do not read code or long text'). It also gives operational guidance about calling early instead of waiting for full-task completion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the immediate stop action, the default clear_queue=true, and the conditional behavior when false ('only stop current and keep the queue'). This is meaningful behavioral detail beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action, and no filler. Every word adds value, covering the action and the parameter behavior efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, this is complete: it states the action, the parameter semantics, and the default. Since an output schema exists, return-value documentation is not required from the description, and nothing essential for calling the tool is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully explains the single parameter clear_queue: its default value and the effect when false. This completely compensates for the schema's lack of semantic detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Immediately stop current TTS playback.' This clearly distinguishes it from the sibling tool speak, leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool, and the only sibling (speak) makes the use case obvious. While it does not explicitly name an alternative, the parameter guidance about clearing vs. keeping the queue adds practical usage context.
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
Copy to your README.md:
Score Badge
Copy to your README.md: