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
concisr

Or 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