Concisr
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.
Why?
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.
Tools
| Tool | Purpose | Compression | |------|---------|------------| | digest_input | Strip incoming messages to essential signal | ~25-95% depending on mode | | compress_response | Compress outgoing responses with sentence truncation | Preserves voice and meaning | | cache_reference | Gzip-compressed key-value store with TTL expiry | Store large text, retrieve on demand | | session_stats | Real-time token savings dashboard | Track ROI across sessions |
Compression Modes
| Mode | Level | Strategy | |------|-------|----------| | checkin | ~25% | Extract structured metrics (pain, sleep, energy, food, weight, stress) | | task | ~50% | Strip filler words, greetings, hedges | | casual | ~75% | Light structural compression | | narrative | ~95% | Preserve detail with minimal trimming |
Content-type detection automatically applies JSON crushing, code comment stripping, or prose pass-through.
Quick Start
# Install via pip
pip install concisr
# Run locally
concisrOr add to your MCP client config:
{
"mcpServers": {
"concisr": {
"url": "https://concisr.mcpize.run/mcp"
}
}
}Deployed
Live on MCPize — Free tier (500 req/mo) and Pro ($50/mo unlimited).
Storage
- Cache DB:
~/.concisr/cache.db(SQLite, gzip-compressed blobs) - Stats:
~/.concisr/stats.json(persistent across sessions)
Author
Eric Ian Rodriguez
- Portfolio: tiny-bavarois-656e6c.netlify.app
- GitHub: github.com/NcrMancer
License
MIT
