decompose-mcp

Decompose

Stop prompting. Start decomposing.

The missing cognitive primitive for AI agents. Decompose turns any text into classified, structured semantic units — instantly. No LLM. No setup. One function call.

Before: your agent reads this

The contractor shall provide all materials per ASTM C150-20. Maximum load
shall not exceed 500 psf per ASCE 7-22. Notice to proceed within 14 calendar
days of contract execution. Retainage of 10% applies to all payments.
For general background, the project is located in Denver, CO...

After: your agent reads this

[
  {
    "text": "The contractor shall provide all materials per ASTM C150-20.",
    "authority": "mandatory",
    "risk": "compliance",
    "type": "requirement",
    "irreducible": true,
    "attention": 8.0,
    "entities": ["ASTM C150-20"]
  },
  {
    "text": "Maximum load shall not exceed 500 psf per ASCE 7-22.",
    "authority": "prohibitive",
    "risk": "safety_critical",
    "type": "constraint",
    "irreducible": true,
    "attention": 10.0,
    "entities": ["ASCE 7-22"]
  }
]

Every unit classified. Every standard extracted. Every risk scored. Your agent knows what matters.

Install

pip install decompose-mcp

Use as MCP Server

Add to your agent's MCP config (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "decompose": {
      "command": "uvx",
      "args": ["decompose-mcp", "--serve"]
    }
  }
}

Your agent gets two tools:

• decompose_text — decompose any text
• decompose_url — fetch a URL and decompose its content

OpenClaw

Install the skill from ClawHub or configure directly:

{
  "mcpServers": {
    "decompose": {
      "command": "python3",
      "args": ["-m", "decompose", "--serve"]
    }
  }
}

Or install the skill: clawdhub install decompose-mcp

Use as CLI

# Pipe text
cat spec.txt | decompose --pretty

# Inline
decompose --text "The contractor shall provide all materials per ASTM C150-20."

# Compact output (smaller JSON)
cat document.md | decompose --compact

Use as Library

from decompose import decompose

result = decompose("The contractor shall provide all materials per ASTM C150-20.")

for unit in result["units"]:
    print(f"[{unit['authority']}] [{unit['risk']}] {unit['text'][:60]}...")

What Each Field Means

Field	Values	What It Tells Your Agent
authority	mandatory, prohibitive, directive, permissive, conditional, informational	Is this a hard requirement or background?
risk	safety_critical, security, compliance, financial, contractual, advisory, informational	How much does this matter?
type	requirement, definition, reference, constraint, narrative, data	What kind of content is this?
irreducible	true/false	Must this be preserved verbatim?
attention	0.0 - 10.0	How much compute should the agent spend here?
entities	standards, codes, regulations	What formal references are cited?
actionable	true/false	Does someone need to do something?

Why No LLM?

Decompose runs on pure regex and heuristics. No Ollama, no API key, no GPU, no inference cost.

This is intentional:

• Fast: <500ms for a 50-page spec
• Deterministic: Same input always produces same output
• Offline: Works air-gapped, on a plane, on CI
• Composable: Your agent's LLM reasons over the structured output — decompose handles the preprocessing

The LLM is what your agent uses. Decompose makes whatever model you're running work better.

Built by Echology

Decompose is extracted from AECai, a document intelligence platform for Architecture, Engineering, and Construction firms. The classification patterns, entity extraction, and irreducibility detection are battle-tested against thousands of real AEC documents — specs, contracts, RFIs, inspection reports, pay applications.

Blog

• When Regex Beats an LLM — Decompose classifies the MCP spec in 3.78ms
• Why Your Agent Needs a Cognitive Primitive — attention scoring, irreducibility, and routing
• What "Simulation-Aware" Actually Means — the architecture behind AECai

License: Proprietary — Copyright (c) 2025-2026 Echology, Inc.

Philosophy: All intelligence begins with decomposition.