Decompose

Decompose any text or URL into classified semantic units. Each unit gets authority level, risk category, attention score, entity extraction, and irreducibility flags. No LLM required. Deterministic. Runs locally.

Setup

1. Install

pip install decompose-mcp

2. Configure MCP Server

Add to your OpenClaw MCP config:

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

3. Verify

python3 -m decompose --text "The contractor shall provide all materials per ASTM C150-20."

Available Tools

decompose_text

Decompose any text into classified semantic units.

Parameters:

• text (required) — The text to decompose
• compact (optional, default: false) — Omit zero-value fields for smaller output
• chunk_size (optional, default: 2000) — Max characters per unit

Returns: JSON with units array. Each unit contains:

• authority — mandatory, prohibitive, directive, permissive, conditional, informational
• risk — safety_critical, security, compliance, financial, contractual, advisory, informational
• attention — 0.0 to 10.0 priority score
• actionable — whether someone needs to act on this
• irreducible — whether content must be preserved verbatim
• entities — referenced standards and codes (ASTM, ASCE, IBC, OSHA, etc.)
• dates — extracted date references
• financial — extracted dollar amounts and percentages
• heading_path — document structure hierarchy

decompose_url

Fetch a URL and decompose its content. Handles HTML, Markdown, and plain text.

Parameters:

• url (required) — URL to fetch and decompose
• compact (optional, default: false) — Omit zero-value fields

What It Detects

• Authority levels — RFC 2119 keywords: "shall" = mandatory, "should" = directive, "may" = permissive
• Risk categories — safety-critical, security, compliance, financial, contractual
• Attention scoring — authority weight x risk multiplier, 0-10 scale
• Standards references — ASTM, ASCE, IBC, OSHA, ACI, AISC, AWS, ISO, EN
• Financial values — dollar amounts, percentages, retainage, liquidated damages
• Dates — deadlines, milestones, notice periods
• Irreducibility — legal mandates, threshold values, formulas that cannot be paraphrased

Use Cases

• Pre-process documents before sending to your LLM — save 60-80% of context window
• Classify specs, contracts, policies, regulations by obligation level
• Extract standards references and compliance requirements
• Route high-attention content to specialized analysis chains
• Build structured training data from raw documents

Performance

• ~14ms average per document on Apple Silicon
• 1,000+ chars/ms throughput
• Zero API calls, zero cost, works offline
• Deterministic — same input always produces same output

Security & Trust

Text classification is fully local. The decompose_text tool performs all processing in-process with no network I/O. No data leaves your machine.

URL fetching performs outbound HTTP requests. The decompose_url tool fetches the target URL, which necessarily involves network I/O to the specified host. This is why the skill declares the network permission in claw.json. If you do not need URL fetching, you can use decompose_text exclusively with no network access required.

SSRF protection. URL fetching blocks private/internal IP ranges before connecting: 0.0.0.0/8, 10.0.0.0/8, 100.64.0.0/10, 127.0.0.0/8, 169.254.0.0/16, 172.16.0.0/12, 192.168.0.0/16, ::1/128, fc00::/7, fe80::/10. The implementation resolves the hostname via DNS before connecting and checks all returned addresses against the blocklist. See src/decompose/mcp_server.py lines 19-49.

No API keys or credentials required. No external services are contacted except when using decompose_url to fetch user-specified URLs.

Source code is fully auditable. The complete source is published at github.com/echology-io/decompose. The PyPI package is built from this repo via GitHub Actions (publish.yml) using PyPI Trusted Publishers (OIDC), so the published artifact is traceable to a specific commit.

Resources

• Source Code (GitHub) — full source, auditable
• PyPI — published via Trusted Publishers
• Documentation
• Blog: When Regex Beats an LLM
• Blog: Why Your Agent Needs a Cognitive Primitive