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

Example prompt: "Decompose this spec and tell me which sections are mandatory"

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

Example prompt: "Decompose https://spec.example.com/transport and show me the security requirements"

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

decompose-mcp

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