# echology-io/decompose [Health: Active] **Category:** 🔎 Search & Data Extraction **Repository:** https://github.com/echology-io/decompose **GitHub Stars:** 10 **Views:** 1 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/echology-io-decompose ## Description Decompose text into classified semantic units with authority, risk, attention scores, and entity extraction. No LLM. Deterministic. Works as MCP server or CLI. ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `uvx` (confidence: high): ```json "mcpServers": { "decompose": { "command": "uvx", "args": ["decompose-mcp","--serve"] } } ``` ## Documentation & README # Decompose [![CI](https://github.com/echology-io/decompose/actions/workflows/ci.yml/badge.svg)](https://github.com/echology-io/decompose/actions/workflows/ci.yml) [![PyPI](https://img.shields.io/pypi/v/decompose-mcp)](https://pypi.org/project/decompose-mcp/) [![Python](https://img.shields.io/pypi/pyversions/decompose-mcp)](https://pypi.org/project/decompose-mcp/) **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 ```json [ { "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 ```bash pip install decompose-mcp ``` ## Use as MCP Server Add to your agent's MCP config (Claude Code, Cursor, Windsurf, etc.): ```json { "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: ```json { "mcpServers": { "decompose": { "command": "python3", "args": ["-m", "decompose", "--serve"] } } } ``` Or install the skill: `clawdhub install decompose-mcp` ## Use as CLI ```bash # 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 ```python 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. ```python 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. ```python 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 ```python 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/`](https://github.com/echology-io/decompose/blob/HEAD/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](https://echology.io) and extracted from [AECai](https://aecai.io), 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](https://github.com/echology-io/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. ### Blog - [When Regex Beats an LLM](https://echology.io/blog/regex-beats-llm) — Decompose classifies the MCP spec in 3.78ms - [Why Your Agent Needs a Cognitive Primitive](https://echology.io/blog/cognitive-primitive) — attention scoring, irreducibility, and routing - [What "Simulation-Aware" Actually Means](https://echology.io/blog/simulation-aware) — the architecture behind AECai **License:** MIT — Copyright (c) 2025-2026 Echology, Inc.