ai_voiceText-to-speech voiceover MP3 (ElevenLabs Turbo 2.5, 32 languages, up to 1000 chars). ~$0.05.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to speak | |
| voice | No | Voice name, e.g. Rachel (default), Adam, Bella |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is not read-only and may have side effects, but the description adds valuable behavioral context: it produces an MP3, specifies the underlying model, supports 32 languages, and has a 1000-character limit. It also discloses the approximate cost (~$0.05), which is useful for planning. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that packs essential information: output format, model, language count, character limit, and cost. Every word contributes value, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no nested objects) and the presence of an output schema and annotations, the description is largely complete. It covers model, output type, languages, length limit, and cost. Missing explicit guidance on when to choose this over sibling media tools, but that gap is minor for such a straightforward TTS tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters (text and voice) with descriptions, so the baseline is 3. The description adds meaningful constraint for the text parameter ('up to 1000 chars'), which is not stated in the schema. This extra detail enhances parameter understanding beyond schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Text-to-speech voiceover MP3' clearly states the tool's function with a specific verb and output format, distinguishing it from sibling tools like ai_image and ai_music. It also provides concrete details (ElevenLabs Turbo 2.5, 32 languages, char limit) that reinforce the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the text-to-speech description, but there are no explicit statements about when to use this tool versus alternatives like ai_music or ai_video. The description doesn't mention exclusions or provide alternative tool references, so guidance remains indirect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.