A
license-
qualityD
maintenanceEnables AI agents to maintain context across conversation sessions by saving and retrieving summaries of key points from past interactions.
Last updated
329
MIT
tibet-voice-cache-mcp
by jaspertvdm
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@tibet-voice-cache-mcpInject 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
MCP server for persistent voice conversation memory. Plug into Claude Code, Cursor, Windsurf, or any MCP client.
pip install tibet-voice-cache-mcpWhat 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: Memory Crystal 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 |
| List all active caches with stats |
| Open/create cache for an actor |
| Record user or AI utterance |
| Mark turn boundary |
| Get formatted context summary |
| Inject context into system instruction |
| Bulk import session transcripts |
| View cached utterances |
| Clear cache for an actor |
| 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 |
| Sectioned with headers (default) |
| Minimal tokens, single-line |
| Natural language, conversational |
| Numbered turn pairs |
Multi-language
voice_cache_configure(actor="user_123", language="nl")Built-in: English (en), Dutch (nl).
Environment variables
Variable | Default | Description |
| (none — RAM only) | Directory for JSON persistence |
|
| Max utterances per side before trimming |
|
| Default summary style |
Resources
The server also exposes MCP resources:
voice-cache://actors— List all actors with open cachesvoice-cache://actor/{name}— Full cache content for an actor
Part of the TIBET ecosystem
Package | Description |
Core library — voice conversation memory | |
| This package — MCP server wrapper |
License
MIT — plug it in, give your voice AI a memory.
This server cannot be installed
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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