Strategic Agent Reasoning MCP
Strategic BDI reasoning engine for autonomous AI agents with spatial utility and replanning.
التثبيت
أضف هذا إلى إعدادات MCP في عميلك (Claude Desktop: claude_desktop_config.json، وCursor: .cursor/mcp.json، ويستخدم VS Code وغيره الصيغة نفسها «mcpServers»).
@putervision/agent-reasoning-mcpclaude mcp add strategic-agent-reasoning-mcp -- npx -y @putervision/agent-reasoning-mcp@0.4.1{
"mcpServers": {
"strategic-agent-reasoning-mcp": {
"command": "npx",
"args": [
"-y",
"@putervision/agent-reasoning-mcp@0.4.1"
]
}
}
}التوثيق
مأخوذ من ملف README للمشروع على GitHub ويُحدَّث أسبوعيًا. تفتح الروابط على GitHub.
المحتويات
@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
⚡ 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🛠️ 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, andNoulprimitives 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.
🏛️ 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
📚 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.jsonparameters and environment variables.
🔗 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 to1(orOFFLINE=1) to force air-gapped L1/L2 deterministic evaluation.
🧪 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📄 License
MIT © PuterVision
الأسئلة الشائعة
ما هو Strategic Agent Reasoning MCP؟
Strategic BDI reasoning engine for autonomous AI agents with spatial utility and replanning.
كيف أثبّت Strategic Agent Reasoning MCP؟
يعمل على جهازك. في Claude Code: claude mcp add strategic-agent-reasoning-mcp -- npx -y @putervision/agent-reasoning-mcp@0.4.1. وفي العملاء الآخرين، الصق إعداد JSON من هذه الصفحة في إعدادات MCP الخاصة بهم.
هل يحتاج Strategic Agent Reasoning MCP إلى مفتاح API؟
لا يذكر إدراجه في السجل أي مفتاح أو سر إلزامي. قد تتطلب بعض الميزات حسابًا لدى الخدمة التي يتصل بها.
هل Strategic Agent Reasoning MCP مفتوح المصدر؟
نعم. شيفرته متاحة على GitHub بترخيص MIT.
من يصون Strategic Agent Reasoning MCP؟
ينشره putervision. يدرجه AI.tn لكنه لا يصونه ولا يوصي به.
من سجل MCP الرسمي. الإدراج ليس توصية: راجع شيفرة الخادم وصلاحياته قبل منحه الوصول إلى بياناتك.