Stop prompting.
Start decomposing.

Decompose is the missing cognitive primitive for AI agents. Text in, classified structured units out. No LLM. No setup. One function call.

pip install decompose-mcp View on GitHub

Before / After

Real output from the MCP Transport Specification.

What your agent sees today
Your agent gets this blob. It has to read every word to figure out what matters. ## Overview MCP provides a standardized way for applications to share contextual information with language models, expose tools and capabilities to AI systems, and build composable integrations. ## stdio Transport Messages are delimited by newlines and MUST NOT contain embedded newlines. The server MUST NOT write anything to stdout that is not a valid MCP message. The client MUST NOT write anything to the server stdin that is not a valid message. ## Security Warning Servers MUST validate the Origin header on all incoming connections to prevent DNS rebinding attacks. Servers SHOULD bind only to localhost. Servers SHOULD implement proper authentication. Without these protections, attackers could use DNS rebinding to interact with local MCP servers. ## Implementation Guidelines Implementors SHOULD build robust consent and authorization flows. They should provide clear documentation and implement appropriate access controls.
What decompose returns
4 units. Your agent reads one. Unit 1 — Overview attention: 0.0 authority: informational risk: informational type: narrative → skip Unit 2 — stdio Transport attention: 0.9 authority: prohibitive risk: informational type: requirement → low priority Unit 3 — Security Warning attention: 4.5 authority: directive risk: security type: requirement actionable: true → read this Unit 4 — Implementation Guidelines attention: 0.4 authority: directive risk: informational type: informational → low priority

attention — 0–10 priority score. Unit 3 scores 4.5 (security risk + directive authority). The overview scores 0.0. Your agent knows where to focus.

risk: security — "attackers", "DNS rebinding", "authentication" trigger security risk detection. The overview and guidelines carry no risk signal.

authority — MUST/MUST NOT = mandatory/prohibitive. SHOULD = directive. Plain prose = informational. Your agent knows what's binding vs. advisory.

actionable — Unit 3 requires action: validate Origin headers, bind to localhost, implement auth. The overview requires nothing.

source — This is real output. Run it yourself: curl spec.modelcontextprotocol.io | python -m decompose

What it does

Every text becomes structured intelligence.

Classify

Authority Detection

Mandatory, prohibitive, directive, permissive, informational, conditional. Knows the difference between "shall" and "should" and "may."

Classify

Risk Scoring

Safety-critical, compliance, financial, contractual, advisory. Each chunk gets scored and labeled by risk category.

Extract

Entity Recognition

Standards, dates, dollar amounts, percentages. Deterministic regex. No hallucinations. No API calls.

Analyze

Irreducibility

Detects content that must be preserved verbatim — legal mandates, threshold values, safety limits. Tells your model what it cannot summarize.

Chunk

Semantic Splitting

Header-aware Markdown chunking. Sentence-boundary text splitting. Each chunk preserves its heading path and structural context.

Score

Attention Budget

Every unit gets an attention score from 0–10. Your agent knows which chunks matter most without reading all of them.

Install

One line. Zero config.

# Install pip install decompose-mcp # Use from Python from decompose import decompose_text result = decompose_text(policy_text) # Use from CLI cat agent_policy.md | python -m decompose --compact # Use as MCP tool python -m decompose --serve

MCP Integration

Works with any MCP-compatible agent.

Add one block to your MCP config. Your agent gets two tools: decompose_text and decompose_url.

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

Benchmarks

Real numbers from the test suite. 5 documents, 15,683 characters, run on Apple Silicon.

2.8ms
Avg per document
1,132
Chars/ms throughput
34
Units from 5 docs
56%
Flagged irreducible
$0
Compute cost
48
Passing tests

All intelligence begins with decomposition.

Your model is only as good as what you feed it. Feed it structure.

pip install decompose-mcp GitHub