Click on "Deploy 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., "@Concisr digest my latest conversation thread"
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.
Token compression for AI contexts. Reduce token consumption by compressing conversation exchanges before they enter the LLM context window.
Deterministic, embedding-free compression. No external APIs, no GPU required.
Every token costs money. Most conversation context is filler — greetings, hedges, repeated data, verbose JSON. Concisr strips that noise before it hits the model, so you fit more signal into fewer tokens.
Related MCP server: SlimContext MCP Server
Tool | Purpose | Compression |
| Strip incoming messages to essential signal | ~25-95% depending on mode |
| Compress outgoing responses with sentence truncation | Preserves voice and meaning |
| Gzip-compressed key-value store with TTL expiry | Store large text, retrieve on demand |
| Real-time token savings dashboard | Track ROI across sessions |
Mode | Level | Strategy |
| ~25% | Extract structured metrics (pain, sleep, energy, food, weight, stress) |
| ~50% | Strip filler words, greetings, hedges |
| ~75% | Light structural compression |
| ~95% | Preserve detail with minimal trimming |
Content-type detection automatically applies JSON crushing, code comment stripping, or prose pass-through.
# Install via pip
pip install concisr
# Run locally
concisrOr add to your MCP client config:
{
"mcpServers": {
"concisr": {
"url": "https://concisr.mcpize.run/mcp"
}
}
}Live on MCPize — Free tier (500 req/mo) and Pro ($50/mo unlimited).
Cache DB: ~/.concisr/cache.db (SQLite, gzip-compressed blobs)
Stats: ~/.concisr/stats.json (persistent across sessions)
Eric Ian Rodriguez
Portfolio: tiny-bavarois-656e6c.netlify.app
GitHub: github.com/NcrMancer
MIT
This server cannot be deployed
Shared distillation cache for AI agents — every fetch ~73-89% fewer tokens via a shared cache.
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Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
