ai_speech
Read-only
Convert text to natural-sounding speech audio. Returns base64-encoded WAV.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to convert to speech |
MapleFlow
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return format (base64-encoded WAV) beyond the annotations. The readOnlyHint annotation aligns with the non-mutating nature of the operation, and no contradictions are present.
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 two concise sentences, front-loaded with the main action and no wasted words.
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?
For a simple single-parameter tool with no output schema, the description covers purpose and return format sufficiently. It could mention limits or usage constraints, but the essential information is present.
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 schema fully describes the single parameter 'text' with 100% coverage, and the description does not add additional semantics beyond what the schema already provides. Baseline 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 converts text to speech audio, using a specific verb and resource, and specifies the output format (base64-encoded WAV), which clearly differentiates it from sibling tools like ai_transcribe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context implies use for text-to-speech synthesis, but it does not explicitly mention alternatives or when not to use it. However, the sibling names provide clear differentiation, so the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4/5.0
Disambiguation5/5
Each tool targets a distinct capability: chat, embeddings, image generation, speech synthesis, transcription, currency exchange, translation, and weather. There is no overlap or ambiguity between them.
Naming Consistency4/5
The five AI tools follow a consistent 'ai_' prefix pattern (ai_chat, ai_embeddings, etc.), but the three utility tools (currency, translate, weather) break this convention, creating a minor inconsistency.
Tool Count5/5
With 8 tools, the server is well-scoped for a multi-purpose AI and utility toolkit. The count is neither too sparse nor overly heavy, and each tool has a clear role.
Completeness4/5
The toolset covers a broad range of AI modalities (text, embedding, image, audio) plus common utilities (currency, translation, weather). Minor gaps exist, such as video generation or web search, but these are not essential for the apparent scope.