@putervision/agent-reasoning-mcp

Strategic BDI Reasoning, Multi-Attribute Expected Utility Theory & Decision Intelligence for Autonomous AI Agents

@putervision/agent-reasoning-mcp is a formal Model Context Protocol (MCP) server that provides strategic belief-desire-intention (BDI) reasoning, hierarchical goal decomposition, multi-attribute expected utility calculation ((E[U] = \sum w_i u_i)), exponential belief decay, quantitative risk evaluation, and reactive replanning across multi-modal memory bridges.

🌐 Official Documentation: putervision.com • Interactive Web Docs

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⚡ 15-Second Quick Start

# 1. Initialize reasoning database & seed default utility profiles
npx @putervision/agent-reasoning-mcp init

# 2. Run health diagnostics and Merkle audit checks
npx @putervision/agent-reasoning-mcp doctor

# 3. Inspect active goals, intentions, and belief states
npx @putervision/agent-reasoning-mcp inspect

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🛠️ 15 Core MCP Tools

BDI Strategic Deliberation (10 Tools)

Tool	Actions	Purpose
set_goal	create, update, decompose, get, list, abandon	Manage goal hierarchy, task DAGs, and success criteria
evaluate_situation	snapshot, quick	Score and rank candidate actions from environment snapshots
replan	blocker, event, full	Adaptively reconstruct subgoals upon obstacles and abort stale intentions
assess_risk	action, plan, compare	Quantitative threat and risk calculation across candidate actions
query_knowledge	search, patterns, similar_situations	Search learned heuristics, tactical knowledge, and past decision patterns
set_utility_weights	configure, get, list, activate	Configure utility weights (aggression, caution, greed, efficiency, exploration)
get_decision_trace	latest, get, list, explain	Explainable chain-of-thought rationale and latency telemetry
manage_beliefs	update, query, expire, reconcile	Structured belief state with exponential confidence decay ($C = C_0 e^{-\lambda t}$)
manage_intentions	create, dispatch, get, list, cancel, resolve	Wire contract directives queue for runtime execution engines
manage_reasoning_db	stats, audit, snapshot, restore	Reasoning database statistics, SHA-256 Merkle audit, and snapshot rollback

System 1 Fast Decision Layer (5 Tools)

Inspired by the typed System 1 pattern pioneered by TypeSafe's Jev (evaluating typed Choice, Score, and Noul primitives over compact state without token generation), implemented locally via in-memory LRU caches and deterministic heuristics (<2ms) without external API calls.

Tool	Purpose	Latency Target	L1 Cache (p50)	Throughput
classify	Low-latency categorical labeling over multi-modal StatePacks	<2ms	0.0075 ms	~90,000 ops/s
ask_noul	Typed probabilistic hypothesis and Boolean verification ($p \in [0.0, 1.0]$)	<2ms	0.0049 ms	~127,000 ops/s
ask_choice	Discrete $1$-of-$N$ choice selection ($N \le 16$) with probability simplex	<2ms	0.0138 ms	~64,000 ops/s
ask_score	Bounded numeric scalar scoring and calibrated utility rating	<2ms	0.0057 ms	~129,000 ops/s
gate_intention	Pre-dispatch blast-radius audit gate issuing signed HMAC dispatch tokens	<1ms	0.0709 ms	~12,000 ops/s

See docs/benchmarks.md for full benchmark reproduction commands, latency percentiles (p50/p95/p99), and multi-tier caching architecture details.

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🏛️ PuterVision Pentad Multi-Modal Ecosystem

agent-reasoning-mcp coordinates the closed-loop PuterVision Super-Loop:

• 🧠 agent-reasoning-mcp: Decides what to do (BDI Strategic Reasoning, Utility Theory, Replanning)
• ⚡ behavior-mcp: Executes how to act at ~60Hz in browser runtimes
• 📊 state-memory-mcp: Durable workflow memory, tasks, blockers, decisions
• 👁️ vision-memory-mcp: Perceptual caching, visual grounding, video timelines
• 🌐 world-model-mcp: 3D/2D spatial layout, entity permanence, collision simulation

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📚 Deep Documentation Guides

• 📖 Formal API Reference: Full parameter tables, type definitions, and tool schemas for all 15 tools.
• 🚀 Performance Benchmarks: Empirical throughput and microsecond latency metrics across all 5 System 1 tools.
• 💡 Core Architecture & Concepts: BDI model, utility formulation, and belief decay dynamics.
• 🖥️ CLI Usage Guide: Complete CLI command reference (init, doctor, inspect, run).
• 💾 Database Schema: SQLite table structures, indexes, and Merkle audit ledger.
• ⚙️ Configuration Reference: .agent-reasoning-mcp.json parameters and environment variables.

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🔗 Client Configuration & Environment

Add to .cursor/mcp.json or .vscode/mcp.json:

{
  "mcpServers": {
    "agent-reasoning-mcp": {
      "command": "agent-reasoning-mcp",
      "args": ["run"],
      "env": {
        "PENTAD_HMAC_SECRET": "your-secure-shared-secret-here",
        "DISPATCH_TOKEN_TTL_MS": "30000"
      }
    }
  }
}

Key Environment Variables

• PENTAD_HMAC_SECRET: 256-bit shared key for cryptographic intention dispatch token signing.
• DISPATCH_TOKEN_TTL_MS: Dispatch token expiration window (default: 30,000ms).
• SKIP_MODEL_LOAD: Set to 1 (or OFFLINE=1) to force air-gapped L1/L2 deterministic evaluation.

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🧪 Testing & Benchmarks

# Run full unit and integration test suites
npm test

# Run System 1 fast decision layer benchmark suite (throughput & latency percentiles)
npm run benchmark

# Run air-gapped verification
OFFLINE=1 SKIP_MODEL_LOAD=1 npm test

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📄 License

MIT © PuterVision