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Strategic BDI reasoning engine for autonomous AI agents — goals, utility, and replanning.
About
Strategic BDI reasoning engine for autonomous AI agents — goals, utility, and replanning.
Security Report
This MCP server implements a sophisticated BDI reasoning engine with proper authentication patterns and reasonable permissions for its purpose. Code quality is generally solid with appropriate error handling and input validation. However, several moderate concerns exist: file I/O operations lack comprehensive permission scoping, the CLI init system writes to multiple IDE config directories without strict validation, and some database operations could benefit from stricter access controls. These issues are not critical but warrant user awareness. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 1 high severity). Package verification found 1 issue.
6 files analyzed · 10 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-putervision-agent-reasoning-mcp": {
"args": [
"-y",
"@putervision/agent-reasoning-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
@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
🛠️ 10 Core MCP 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 |
🏛️ 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.
- 💡 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
Add to .cursor/mcp.json or .vscode/mcp.json:
{
"mcpServers": {
"agent-reasoning-mcp": {
"command": "agent-reasoning-mcp",
"args": ["run"]
}
}
}
🧪 Testing
# Run full unit and integration test suite across 16 test files (68 tests)
npm test
📄 License
MIT © PuterVision
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