MCP 服务器

decompose

Classifies text into structured semantic units with authority, risk, and attention scores. Enables deterministic preprocessing for AI agents to filter and route content without using an LLM.

README

Decompose

Stop prompting. Start decomposing.

Deterministic text classification 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_text, filter_for_llm

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

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

# Pre-filter for LLM context — keep only high-value units
filtered = filter_for_llm(result, max_tokens=4000)
print(f"{filtered['meta']['reduction_pct']}% token reduction")
llm_input = filtered["text"]  # Ready for your LLM

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?

What to Build With This

Decompose is not the destination. It's the step before the LLM that most developers skip — not because it's hard, but because nobody showed them it exists. Documents have structure. That structure is classifiable. And classification should happen before reasoning.

Without:  document → chunk → embed → retrieve → LLM → answer  (100% of tokens)
With:     document → decompose → filter/route → LLM → answer  (20-40% of tokens)

Filter: built-in LLM pre-filter

filter_for_llm() keeps mandatory, safety-critical, financial, and compliance units — drops boilerplate before it reaches your LLM or vector store.

from decompose import decompose_text, filter_for_llm

result = decompose_text(open("contract.md").read())
filtered = filter_for_llm(result, max_tokens=4000)

# filtered["text"] = high-value units only, ready for LLM
# filtered["meta"]["reduction_pct"] = how much was dropped (typically 60-80%)

# Or use the units directly for embedding
for unit in filtered["units"]:
    embed_and_store(unit["text"], metadata={
        "authority": unit["authority"],
        "risk": unit["risk"],
        "attention": unit["attention"],
    })

Route: risk-based processing

Safety-critical content goes to one chain. Financial content goes to another. Boilerplate gets skipped.

from decompose import decompose_text

result = decompose_text(spec_text)

for unit in result["units"]:
    if unit["risk"] == "safety_critical":
        safety_chain.process(unit)       # Full analysis + human review
    elif unit["risk"] == "financial":
        audit_chain.process(unit)         # Flag for finance team
    elif unit["attention"] < 0.5:
        pass                              # Skip boilerplate
    else:
        general_chain.process(unit)       # Standard LLM analysis

Measure: token cost reduction

from decompose import decompose_text

result = decompose_text(spec_text)
total = len(result["units"])
high = [u for u in result["units"] if u["attention"] >= 1.0]

print(f"{len(high)}/{total} units need LLM analysis")
print(f"{100 - len(high) * 100 // total}% token reduction")

See examples/ for runnable scripts.

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 built by Echology and 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.

Decompose earned its independence — it started as AECai's text classification module, proved general enough to work across domains (insurance, trading, regulatory), and was released standalone. Free, MIT-licensed.

Case Study: Open Scripture Intelligence

The same chunking and entity extraction patterns that classify engineering specs also structure the Bible. Open Scripture Intelligence uses Decompose's Markdown-aware chunker and regex entity extraction to transform 31,100 verses into a knowledge graph with 344,799 cross-reference edges and semantic embeddings — proving the methodology is domain-agnostic.