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Neuratel-AI

Neuratel MCP Server

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by Neuratel-AI

create_agent

Create a voice AI agent with just a name and instructions. Automatically applies provider defaults for brain, voice, and transcriber, enabling immediate deployment.

Instructions

Create a new voice AI agent.

Only name and instructions are required. All provider defaults are applied server-side — if you don't specify brain/voice/transcriber, the platform picks its current production defaults (Phantom brain, Cartesia sonic-3 voice with Fatima — Arabic, Soniox stt-rt-v4 transcriber with EN+AR language hints — pairs natively with Fatima for code-switching). Behavior defaults: turn_detection.mode=stt (Soniox owns endpointing), preemptive_generation=true, recording on with 30-day retention, post-call analysis on (PassFail rubric).

Brain (LLM) providers

phantom — default, Neuratel AI (~178ms TTFT)

  • model: "phantom"

groq — Groq fast inference (~443ms TTFT)

  • model: "meta-llama/llama-4-scout-17b-16e-instruct" (recommended)

  • model: "llama-3.1-8b-instant" (fastest)

  • model: "openai/gpt-oss-20b" (with reasoning)

openai — OpenAI GPT (~583-1213ms TTFT)

  • model: "gpt-5.4", "gpt-5.4-mini", "gpt-5.4-nano"

  • model: "gpt-4.1", "gpt-4.1-mini", "gpt-4.1-nano"

xai — xAI Grok (~93-180ms TTFT)

  • model: "grok-4-1-fast-non-reasoning" (recommended)

  • model: "grok-4.20-0309-non-reasoning"

Voice (TTS) providers

cartesia — default, best quality (~37ms latency)

  • voice_model: "sonic-3"

  • voice_id: "731ace69-ee17-41bc-8c6f-665c9f1db95c" (default, Fatima — Arabic; pairs with default Soniox EN+AR hints)

  • voice_speed: float or preset ("fastest","fast","normal","slow","slowest")

elevenlabs — most expressive (~71ms latency)

  • voice_model: "eleven_flash_v2_5"

  • voice_id: any ElevenLabs voice ID

  • voice_speed: float (0.25–4.0), stability via config dict

phantom — Neuratel native voices (~100ms latency)

  • voice_model: "phantom-english-speech-preview" or "phantom-arabic-speech-preview"

  • voice_id not used; set voice name in config: {"voice": {"voice": "aria"}}

  • English voices: aria, bella, claire, alex, david, marcus

  • Arabic voices: omar, tariq, layla, nour

Transcriber (STT) providers

soniox — default, Soniox v4 with semantic end-of-utterance built in

  • transcriber_model: "stt-rt-v4" (single unified model, 60+ languages)

  • Default language_hints: ["en", "ar"], language_hints_strict: true

  • When transcriber.provider="soniox", the worker auto-routes turn_detection.mode to "stt" (Soniox owns endpointing)

  • Requires per-org soniox_api_key (BYOK)

deepgram — best accuracy for telephony-only English (~83ms latency)

  • transcriber_model: "nova-3" (recommended), "nova-3-medical"

  • language: BCP-47 e.g. "en-US", "ar", "multi" (auto-detect)

openai — GPT-4o powered (~138ms latency)

  • transcriber_model: "gpt-4o-mini-transcribe"

  • language: ISO code e.g. "en", "ar", "es"

phantom — Neuratel native STT

  • transcriber_model: "phantom-stt-v1"

  • language: "auto" (auto-detect)

Advanced config

Use the config dict for anything not covered by named params. It accepts the full agent config structure — same shape as get_agent returns.

config={
    "turn_detection": {
        "mode": "semantic_vad",   # or "vad"
        "min_delay": 0.5,
        "max_delay": 6.0,
        "endpointing_mode": "dynamic"  # or "fixed"
    },
    "timeout": {
        "enabled": True,
        "trigger_seconds": 15.0,
        "warning_messages": ["Are you still there?"],
        "final_message": "Goodbye!"
    },
    "background_audio": {
        "ambient": {"enabled": True, "source": "office_ambience", "volume": 0.3},
        "thinking": {"enabled": True, "source": "keyboard_typing", "volume": 0.5}
    },
    "tools": {
        "rag": {"enabled": True, "knowledge_base_ids": ["kb-id"], "top_k": 5},
        "voicemail": {"enabled": True, "action": "hangup"},
        "hangup": {"enabled": True, "keywords": ["goodbye", "bye"]}
    },
    "transfer": {
        "enabled": True,
        "mode": "blind",
        "destinations": [{"name": "Support", "number": "+15551234567",
                          "description": "Human agent", "keywords": ["human", "agent"]}]
    },
    "analytics": {
        "recording": {"enabled": True},
        "summary": {"enabled": True},
        "success_evaluation": {
            "enabled": True,
            "criteria": "Did the agent resolve the issue?",
        },
    },
    "interruption": {
        "enabled": True,
        "min_duration": 0.5,
        "min_words": 0,
        "false_interruption_timeout": 2.0,
        "resume_false_interruption": True
    }
}

Returns: agent id, name, status, brain provider/model, voice provider.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
tagsNo
configNo
languageNo
voice_idNo
max_tokensNo
brain_modelNo
descriptionNo
temperatureNo
voice_modelNo
voice_speedNo
instructionsYes
first_messageNo
brain_providerNo
voice_providerNo
max_call_durationNo
transcriber_modelNo
interruption_enabledNo
transcriber_providerNo
first_message_delay_msNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 important behaviors: server-side defaults, turn_detection mode, recording retention, post-call analysis, and BYOK requirements for Soniox. It could also mention side effects like agent uniqueness, but the coverage is strong.

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 long but highly structured with headers, bullet lists, and a code example. It is front-loaded with the core requirement and every section adds necessary detail for a 20-parameter tool. No filler or redundancy.

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?

Given the tool's complexity (20 params, multiple providers, advanced config), the description is remarkably comprehensive. It covers required fields, defaults, provider options, configuration examples, and return values. It fully compensates for the bare schema and lacks only minor exclusions.

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 description coverage is 0%, so the description compensates well. It details valid providers and models for brain, voice, and transcriber, explains the config dict, and clarifies important defaults. However, a few parameters like tags, max_tokens, temperature, and first_message_delay_ms are not explained individually, though they are standard.

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 opens with 'Create a new voice AI agent,' using a specific verb and resource. It clearly distinguishes from siblings like update_agent, delete_agent, and get_agent.

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 description makes it clear this is for creating a new agent, with only name and instructions required. However, it does not explicitly mention when to use update_agent for modifications or list alternative tools, though the create context is obvious.

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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