token-compressor-mcp 0.1.1

MCP server that compresses prompts 40-60% using local LLM + embedding validation

token-compressor

mcp-name: io.github.base76-research-lab/token-compressor

Semantic prompt compression for LLM workflows. Reduce token usage by 40–60% without losing meaning.

Built by Base76 Research Lab — research into epistemic AI architecture.

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What it does

token-compressor is a two-stage pipeline that compresses prompts before they reach an LLM:

1. LLM compression — a local model (llama3.2:1b via Ollama) rewrites the prompt to its semantic minimum, preserving all conditionals and negations
2. Embedding validation — cosine similarity between original and compressed embeddings must exceed a threshold (default: 0.90) — if not, the original is sent unchanged

The result: shorter prompts, lower costs, same intent.

Input prompt (300 tokens)
        ↓
  LLM compresses
        ↓
  Embedding validates (cosine ≥ 0.90?)
        ↓
  Pass → compressed (120 tokens)   Fail → original (300 tokens)

Key design principle: conditionality is never sacrificed. If your prompt says "only do X if Y", that constraint survives compression.

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Requirements

• Python 3.10+
• Ollama running locally
• Two models pulled:

ollama pull llama3.2:1b
ollama pull nomic-embed-text

• Python dependencies:

pip install ollama numpy

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Quick start

from compressor import LLMCompressEmbedValidate

pipeline = LLMCompressEmbedValidate()
result = pipeline.process("Your prompt text here...")

print(result.output_text)   # compressed (or original if validation failed)
print(result.report())      # MODE / COVERAGE / TOKENS saved

Result object:

Field	Description
output_text	Text to send to your LLM
mode	compressed / raw_fallback / skipped
coverage	Cosine similarity (0.0–1.0)
tokens_in	Estimated input tokens
tokens_out	Estimated output tokens
tokens_saved	Difference

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CLI usage

echo "Your long prompt here..." | python3 cli.py

Output: compressed text on stdout, stats on stderr.

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Claude Code hook (recommended setup)

Add to your ~/.claude/settings.json under hooks → UserPromptSubmit:

{
  "type": "command",
  "command": "echo \"${CLAUDE_USER_PROMPT:-}\" | python3 /path/to/token-compressor/cli.py > /tmp/compressed_prompt.txt 2>/tmp/compress.log || true"
}

This runs on every prompt submission and writes the compressed version to a temp file, which can be injected back into context via a second hook or MCP server.

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MCP server

The MCP server exposes compression as a tool callable from Claude Code and any MCP-compatible client.

Install:

pip install token-compressor-mcp

Tool: compress_prompt

• Input: text (string)
• Output: compressed text + stats footer

Claude Code MCP config (~/.claude/settings.json):

{
  "mcpServers": {
    "token-compressor": {
      "command": "uvx",
      "args": ["token-compressor-mcp"]
    }
  }
}

Or from source:

{
  "mcpServers": {
    "token-compressor": {
      "command": "python3",
      "args": ["-m", "token_compressor_mcp"],
      "cwd": "/path/to/token-compressor"
    }
  }
}

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Configuration

pipeline = LLMCompressEmbedValidate(
    threshold=0.90,          # cosine similarity floor (lower = more aggressive)
    min_tokens=80,           # skip pipeline below this (not worth compressing)
    compress_model="llama3.2:1b",
    embed_model="nomic-embed-text",
)

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How it works

Stage 1 — LLM compression

The compression prompt instructs the model to:

• Preserve all conditionals (if, only if, unless, when, but only)
• Preserve all negations
• Remove filler, hedging, redundancy
• Target 40–60% of original length

Stage 2 — Embedding validation

Computes cosine similarity between the original and compressed text using nomic-embed-text. If similarity falls below threshold, the original is returned unchanged. This prevents silent meaning loss.

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Results

Tested across Swedish and English prompts, technical and natural language:

Input	Tokens in	Tokens out	Saved
Research abstract (EN)	89	38	57%
Session intent (SV)	32	18	44%
Technical instruction	47	22	53%
Short command (<80t)	—	—	skipped

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Research background

This tool implements the architecture from:

Wikström, B. (2026). When Alignment Reduces Uncertainty: Epistemic Variance Collapse and Its Implications for Metacognitive AI. DOI: 10.5281/zenodo.18731535

Part of the Base76 Research Lab toolchain for epistemic AI infrastructure.

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License

MIT — Base76 Research Lab, Sweden