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Neuroverse MCP Server

@joshua400

Repository

Basic information

Category

Other

Transports

stdio

Author

joshua400

Submitted by

Joshua Ragiland M

About Neuroverse MCP Server

Multilingual intelligence + memory + safety + voice layer for autonomous AI agents

Neuroverse MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "neuroverse": {
      "command": "npx",
      "args": [
        "-y",
        "neuroverse@latest"
      ],
      "env": {
        "OPENAI_API_KEY": "<YOUR_OPENAI_API_KEY>",
        "REDIS_URL": "redis://localhost:6379",
        "GROQ_API_KEY": "<YOUR_GROQ_API_KEY>"
      }
    }
  }
}

Neuroverse MCP Server tools

11

Process mixed-language input through the full NeuroVerse pipeline. Pipeline: Language Detect → Normalise → Intent Extract → Safety Check → (optional) Execute Supported languages: Tamil, Hindi, Telugu, Kannada + English (code-switched). Args: - text (string): Raw user input, possibly code-switched - user_id (string): User / agent identifier (default: "anonymous") - execute (boolean): Whether to also execute the intent (default: true) Returns: JSON with keys: language, intent, safety, execution (if execute=true) Examples: - "anna indha file ah csv convert pannu" → detects Tamil+English, extracts convert_format - "report banao sales ka" → detects Hindi+English, extracts generate_report - "drop database production" → BLOCKED by safety layer

Store a memory record in NeuroVerse's tiered memory system. Tiers: - short_term: In-process, capped at 50 per user. Lost on restart. - episodic: Persisted to JSON file. Recent actions. - semantic: Persisted to JSON file. Long-term facts. Only episodic/semantic memories with importance_score ≥ 0.4 are persisted. Args: - user_id (string): Agent / user identifier - intent (string): Canonical intent name - tier (string): short_term | episodic | semantic - language (string): Language code (default: "en") - data (object): Structured payload - importance_score (number): 0.0–1.0 Returns: JSON of the stored MemoryRecord

Retrieve memories from NeuroVerse's tiered memory system. Args: - user_id (string): Agent / user identifier - intent (string, optional): Filter by intent - tier (string, optional): Filter by tier - semantic_query (string, optional): Search constraint for vector engine - limit (number): Max results (1–100, default 10) Returns: JSON array of matching MemoryRecords

Parse, safety-check, and execute a user request end-to-end. Convenience tool that chains: Language → Intent → Safety → Execute. Args: - text (string): Raw user input - user_id (string): User / agent identifier Returns: JSON with safety verdict and execution result

Route a task to a registered downstream agent via HTTP. Args: - target_agent (string): Name of the agent - task (string): Task description - payload (object): Arbitrary payload Returns: JSON with the agent's response or a fallback error

Query the multi-model AI router. If a prompt is provided, the prompt is sent to the routed model. Otherwise, returns only the routing decision. Supported providers: OpenAI, Anthropic, Sarvam AI, Ollama. Args: - task_type (string): multilingual | reasoning | local | general - prompt (string, optional): Prompt to send Returns: JSON with routing decision and optional model response

Transcribe an audio file to text using Whisper STT. Args: - audio_path (string): Absolute path to the audio file Returns: JSON with the transcribed text

Synthesize text to speech using Coqui TTS. Args: - text (string): Text to synthesize - language (string): Language code Returns: JSON with the path to the generated audio file

Submit Reinforcement Learning from Human Feedback (RLHF) data for agent tuning.

Scan the codebase and assemble the most relevant file chunks based on a query.

Execute a complex reasoning task using specialized high-performance models (e.g. OpenRouter Reasoning). Returns the model's analytical response.

Overview

What is Neuroverse?

Neuroverse is an MCP server that gives AI agents multilingual intelligence (Tamil, Hindi, Telugu, Kannada, Malayalam, Bengali + English code-switching), tiered memory, a voice layer, intent extraction, multi-model routing, and three-layer safety. It is designed for autonomous agents that need to understand mixed Indian languages, remember context across sessions, and execute actions safely.

How to use Neuroverse?

Install via npm (npm install neuroverse) for a Node.js 18+ environment (zero database deps) or from source (Python 3.10+, requires PostgreSQL for persistent memory). Add the standard MCP stdio configuration to your host (Cursor, VS Code Copilot, Claude Desktop). Then instruct your agent to use MCP tools like neuroverse_process for handling requests and neuroverse_store/neuroverse_recall for persistent context.

Key features of Neuroverse

  • Multilingual Intelligence (Vani) with keyword normalisation for code-switched input
  • Intent Extraction (Bodhi) — LLM-first with deterministic rule-based fallback
  • Tiered Memory (Smriti) — short-term, episodic, semantic with importance scoring
  • 3-Layer Safety (Kavach) — blocklist → risk classifier → LLM judge at zero token cost
  • Multi-Model Router (Marga) — routes tasks to OpenAI, Anthropic, Sarvam AI, Ollama, OpenRouter
  • Agent-to-Agent (Setu) — REST+JSON agent registry for automatic handoff

Use cases of Neuroverse

  • Handling user queries in mixed Indian languages (e.g., "anna indha file ah csv convert pannu")
  • Maintaining agent context across chat sessions via tiered memory
  • Blocking dangerous actions like DROP DATABASE before execution
  • Routing analytical reasoning tasks to OpenRouter and cheaper tasks to local models
  • Enabling voice input (Whisper STT) and voice output (Coqui TTS) for agents

Frequently asked questions

1

What languages does Neuroverse support?

Neuroverse supports Tamil, Hindi, Telugu, Kannada, Malayalam, and Bengali with English code-switching. It normalises only domain-critical keywords rather than full translation.

2

Does Neuroverse require a database?

The npm edition uses JSON files and requires no database. The Python source edition requires PostgreSQL for persistent tiered memory.

3

How does the safety layer work?

Kavach uses three layers: a keyword blocklist, an intent risk classifier, and an LLM judge. It runs at zero token cost and sub-millisecond latency, blocking dangerous actions before execution.

4

What model providers can Neuroverse route to?

The multi-model router (Marga) supports OpenAI, Anthropic, Sarvam AI, Ollama, and OpenRouter. It selects the best model automatically based on task type.

5

What are the system requirements?

Node.js 18+ for the npm edition; Python 3.10+ with PostgreSQL for the source edition. No other runtime dependencies are mandatory.

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