How do I use tibet-voice-cache-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@tibet-voice-cache-mcp Inject voice memory from last session." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

tibet-voice-cache-mcp

by jaspertvdm

Overview Schema Source Related Servers Discussions Admin

Python

Local

tibet-voice-cache-mcp

MCP server for persistent voice conversation memory. Plug into Claude Code, Cursor, Windsurf, or any MCP client.

pip install tibet-voice-cache-mcp

What it does

Gives any MCP-compatible AI client tools to store and recall voice conversation context. User and AI utterances are stored separately in RAM (or optionally on disk) and formatted as clean context summaries — no fake turns, no role confusion.

┌──────────────────────────────────────────────────────────────┐
│  MCP Client (Claude Code / Cursor / Windsurf / etc.)         │
│                                                              │
│  voice_cache_add(actor="user_1", text="...", role="user")    │
│  voice_cache_add(actor="user_1", text="...", role="ai")      │
│  voice_cache_turn(actor="user_1")                            │
│                                                              │
│  voice_cache_inject(actor="user_1",                          │
│    base_instruction="You are a voice assistant.")            │
│  → "You are a voice assistant.                               │
│                                                              │
│     === PRIOR CONTEXT ===                                    │
│     The user previously said:                                │
│       - What's the weather?                                  │
│     You previously responded:                                │
│       - Sunny and 22 degrees!                                │
│     === END CONTEXT ==="                                     │
└──────────────────────────────────────────────────────────────┘

Related MCP server: LedgerMem MCP Server

Setup

Claude Code

// ~/.claude.json
{
  "mcpServers": {
    "voice-cache": {
      "command": "tibet-voice-cache-mcp"
    }
  }
}

With disk persistence

{
  "mcpServers": {
    "voice-cache": {
      "command": "tibet-voice-cache-mcp",
      "env": {
        "VOICE_CACHE_DIR": "/path/to/cache"
      }
    }
  }
}

Cursor / Windsurf

Same pattern — add tibet-voice-cache-mcp as an MCP server command.

Tools

Tool	Description
`voice_cache_status`	List all active caches with stats
`voice_cache_open`	Open/create cache for an actor
`voice_cache_add`	Record user or AI utterance
`voice_cache_turn`	Mark turn boundary
`voice_cache_context`	Get formatted context summary
`voice_cache_inject`	Inject context into system instruction
`voice_cache_session`	Bulk import session transcripts
`voice_cache_history`	View cached utterances
`voice_cache_clear`	Clear cache for an actor
`voice_cache_configure`	Change summary style / language

Quick workflow

# During voice session
voice_cache_open(actor="user_123")
voice_cache_add(actor="user_123", text="What's the weather?", role="user")
voice_cache_add(actor="user_123", text="Sunny and warm!", role="ai")
voice_cache_turn(actor="user_123")

# Next session — inject memory
voice_cache_inject(
    actor="user_123",
    base_instruction="You are a friendly weather assistant."
)

Summary styles

Configure how context is formatted:

voice_cache_configure(actor="user_123", summary_style="compact")

Style	Format
`labeled`	Sectioned with headers (default)
`compact`	Minimal tokens, single-line
`narrative`	Natural language, conversational
`chronological`	Numbered turn pairs

Multi-language

voice_cache_configure(actor="user_123", language="nl")

Built-in: English (en), Dutch (nl).

Environment variables

Variable	Default	Description
`VOICE_CACHE_DIR`	(none — RAM only)	Directory for JSON persistence
`VOICE_CACHE_MAX_TURNS`	`50`	Max utterances per side before trimming
`VOICE_CACHE_STYLE`	`labeled`	Default summary style

Resources

The server also exposes MCP resources:

voice-cache://actors — List all actors with open caches
voice-cache://actor/{name} — Full cache content for an actor

Part of the TIBET ecosystem

Package	Description
`tibet-voice-cache`	Core library — voice conversation memory
`tibet-voice-cache-mcp`	This package — MCP server wrapper

License

MIT — plug it in, give your voice AI a memory.

This server cannot be deployed

Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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Apache 2.0

tibet-voice-cache-mcp

What it does

Setup

Claude Code

With disk persistence

Cursor / Windsurf

Tools

Quick workflow

Summary styles

Multi-language

Environment variables

Resources

Part of the TIBET ecosystem

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Memory MCP Server

LedgerMem MCP Serverofficial

Memory Server

Genomefy MCP Server