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This connector has been deprecated

This connector is a duplicate. Use Hubris instead — same endpoint (https://api.hubris.pw/mcp), but with ownership verified, OAuth, all five tools inspected and a quality score.

Text to speech

speech_create

Read text aloud with a text-to-speech model from the Hubris catalogue and get the audio file back as audio content. Same as POST /v1/audio/speech.

Many models need a voice: the list of voices is on the model card (models_get_pricing → supportedVoices) — pick one from there. The answer is mp3 unless you ask for pcm. Up to 5 000 characters per call, a few minutes of speech; split longer texts or use the HTTP API (POST /v1/audio/speech).

Spends money from the account balance: priced per character of text or per second of audio, depending on the model — models_get_pricing shows which. The charge shows up in usage_get.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesText to read aloud, up to 5 000 characters.
modelYesSpeech model id, vendor included and matched exactly, e.g. "microsoft/mai-voice-2-flash". Find one with models_search and tool: "speech_create".
voiceNoVoice name from the model card. Required when the model has a list of voices.
response_formatNoAudio format: "mp3" (default here, plays everywhere) or "pcm" (raw 16-bit samples).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations mark this as a non-read-only, open-world, non-idempotent operation, and the description goes well beyond them: it discloses that the call spends real account balance, that pricing is per character or per second depending on the model, and where the charge surfaces (usage_get). That is exactly the cost/irreversibility context an agent needs before invoking a billable mutation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short paragraphs, front-loaded with what the tool does, then voice/format specifics, then cost. Every sentence carries information, though the closing cost sentence is slightly redundant with the pricing pointer in the middle paragraph.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-parameter tool with no output schema and no nested objects, the description covers inputs, defaults, limits, cost implications, and the return medium (audio content). Nothing an agent needs in order to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the 3 baseline applies, but the description adds real meaning: it points to models_get_pricing → supportedVoices as the source of valid voice names, explains the mp3-vs-pcm choice, and reinforces the 5 000-character input cap. Only the model-id discovery path is a genuinely new hint beyond the schema text.

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?

States a specific verb and resource ('Read text aloud with a text-to-speech model from the Hubris catalogue and get the audio file back as audio content') and pins it to the underlying operation (POST /v1/audio/speech). This clearly separates it from siblings like transcription_create, image_generate and video_create.

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?

Gives concrete usage context: pick a voice when the model needs one, mp3 is the default unless pcm is requested, and texts beyond 5 000 characters should be split or sent to the HTTP API — an explicit alternative. It stops short of stating when *not* to use this tool versus e.g. transcription_create, so it is clear context rather than full routing guidance.

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

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