generate_speechTurn text into spoken audio with GenMagic. Returns the audio inline plus a hosted URL. The text is voiced verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The text to speak, read aloud verbatim. | |
| voice | No | Optional voice name (e.g. alloy). Defaults to a neutral voice. |
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?
With no annotations, the description carries the burden of behavioral disclosure. It discloses the return format (inline audio plus hosted URL) and a key behavior: the text is voiced verbatim. It does not discuss persistence, rate limits, or authentication, but the core behavior is well covered for a simple generation tool.
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 two sentences with no fluff or redundant content. It front-loads the primary purpose, then adds return behavior and a critical nuance, making it efficient and easy to parse.
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?
The tool is simple with two fully documented parameters and no output schema, so the description's mention of return behavior fills the main gap. It is sufficiently complete for an agent to select and invoke the tool, though it could add notes on URL persistence or audio format.
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?
Schema coverage is 100%, so the parameters are already fully documented. The description adds marginal reinforcement by stating verbatim voicing, but it does not add significant new meaning beyond the schema's own parameter descriptions.
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 clearly states the verb ('Turn text into spoken audio') and the resource, and the title explicitly labels it as text-to-speech. This distinguishes it from sibling tools like generate_text and generate_music without ambiguity.
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?
The description implies when to use the tool: whenever spoken audio from text is needed. However, it does not explicitly mention alternatives or state when not to use it, leaving the choice somewhat to inference rather than direct guidance.
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.
Each tool is primarily tied to a distinct modality or lifecycle step: image, music, speech, text, and video submission/polling. The only minor overlap is that generate_text can also produce SVG graphics, which borders on generate_image's territory, but the type parameter and logo hint keep them separable.
All tool names are lowercase snake_case verb_noun, which is readable and mostly predictable. The slight inconsistency is that four generators use generate_* while the video counterpart uses create_video and get_video instead of generate_video and check_video.
Six tools are well-scoped for a multi-modal generation server: text, image, music, speech, video creation, and video status. Each tool covers a necessary function without redundancy or bloat.
The tool surface covers every advertised generation modality and includes the required async polling lifecycle for video. No obvious dead ends or missing operations are needed for the core generation workflow.