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Behavior Runtime MCP

Runs deterministic behavior trees in browser runtimes with reactive triggers, recordings, telemetry, audit chains, and safety controls.

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Install Behavior Runtime MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "behavior-runtime-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@putervision/behavior-mcp"
      ],
      "env": {
        "PENTAD_HMAC_SECRET": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 10 tools correctly (18d ago).

Overview

Behavior Runtime MCP MCP server executes deterministic behavior trees in browser runtimes at approximately 60Hz. It exposes tools for loading trees, changing parameters, inspecting status, managing triggers and blackboard data, recording actions, collecting metrics, and auditing runtime state. Reach for it when an agent needs controlled, observable behavior-tree execution with pause, resume, abort, rollback, and safety guardrails. The package also provides CLI commands for initialization, diagnostics, inspection, and running the server.

Use cases

  • Execute behavior trees in a browser runtime
  • Configure reactive trigger preemption
  • Inspect active execution and blackboard state
  • Capture deterministic browser action recordings
  • Restore runtime state from checkpoints

Key features

  • Approximately 60Hz behavior-tree execution
  • Reactive priority-based triggers
  • Behavior loading and hot-swapping
  • Blackboard and runtime parameter management
  • SHA-256 Merkle audit chains
  • Safety watchdog and action-rate controls

Tools10

What Behavior Runtime MCP lets your agent do. Expand a tool for its parameters.

Verified live~2.8k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

9/9 checks healthy over the last 44d

What it can do

From the 10 tools this server returned when we ran it; 10 declare their own safety hints.

Can delete or modify data · 3Read-only tools · 7
Network at startup3 connections
Files written at startup18
Install scriptsNone
npm provenanceSigned build
Package age44 days
Maintainers1

Observed 2026-10-25 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 13d ago via OSV.dev · @putervision/behavior-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars8Stargazers on the source repository.
npm downloads3.1kPackage downloads in the last 30 days.
Last commit1mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed10Callable tools this server registers over MCP.

How Behavior Runtime MCP works

What Behavior Runtime MCP MCP server does

Behavior Runtime MCP MCP server connects an MCP client to a browser-based behavior-tree runtime. It executes immutable behavior-tree JSON definitions on a roughly 60Hz loop and supports reactive triggers that can preempt active behavior according to priority. Runtime state includes execution status, node traversal, tick counts, durations, blackboard values, trigger registrations, metrics, recordings, and audit information.

The server is intended for agent-controlled tactical or procedural behavior where execution needs explicit controls and inspection. Behaviors can be loaded, unloaded, or hot-swapped while runtime parameters and shared blackboard values can be queried or changed. Execution can also be aborted, paused, and resumed.

How it works

Behavior Runtime MCP MCP server exposes ten grouped MCP tools. Behavior definitions are registered and retrieved through manage_behaviors, which stores immutable JSON and SHA-256 tree hashes. load_behavior controls which tree is active, while get_status reports the current or historical execution state and tree traversal path.

Reactive interruptions are configured with register_trigger. replay_recording captures or lists deterministic browser action sequences, and get_metrics reports tick timing, telemetry, history, and stuck events. The shared blackboard is managed separately through manage_blackboard.

Runtime persistence and recovery are handled by manage_runtime_db, including SQLite statistics, audit inspection, snapshots, and restore operations. Audit data uses SHA-256 Merkle chains. The execution loop is designed not to wait on external network calls; semantic checks resolve synchronously against blackboard caches.

Setup and configuration

The package requires Node.js 20 or newer. Initialize a workspace and seed its default behavior trees with:

Terminal
npx @putervision/behavior-mcp init

Run diagnostics with npx @putervision/behavior-mcp doctor, inspect active executions with npx @putervision/behavior-mcp inspect, and start the MCP server with npx @putervision/behavior-mcp run.

For MCP client configuration, use the behavior-mcp server name, the behavior-mcp command, and the run argument. Set PENTAD_HMAC_SECRET to the shared secret used to verify signed intention dispatch tokens. Configuration can also be defined through .behavior-mcp.json; the repository provides a separate configuration reference for available settings.

Tools and capabilities

The ten tool groups provide these operations:

  • Load, unload, or swap behavior trees.
  • Set, retrieve, or reset runtime parameters.
  • Query current status, execution history, and tree state.
  • Abort, pause, or resume behavior execution.
  • Register and list priority-based reactive triggers.
  • Capture and list browser action recordings.
  • Read current and historical runtime metrics.
  • Register, retrieve, and list behavior definitions.
  • Read and write blackboard variables.
  • Inspect SQLite data, audit records, snapshots, and restores.

Limitations and notes

Behavior Runtime MCP MCP server applies a fail-closed evaluator: unknown node definitions produce fatal exceptions. Its tick invariant targets approximately 60Hz, and its action rate limit caps throughput at 60 actions per second. Leaf policy controls can block irreversible mutations.

The safety stack also requires valid, unexpired HMAC-signed dispatch tokens, and a watchdog can stop execution when a tick stalls for more than 5,000 milliseconds. An emergency kill switch provides an additional execution halt. These controls mean behavior definitions and dispatch configuration must conform to the runtime’s validation and safety rules.

The repository documents a five-layer guardrail design, SQLite schema, CLI usage, API schemas, and configuration details. It does not describe support for behavior-tree formats other than its JSON definitions or provide client-specific setup beyond the standard MCP command configuration.

Read the full README View source on GitHub

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Frequently Asked Questions about Behavior Runtime MCP

Behavior Runtime MCP is an MCP server that connects AI agents to a browser-based behavior-tree runtime. Its main tools load and swap trees, control execution, register reactive triggers, manage blackboard data, capture recordings, retrieve metrics, and inspect SQLite audit or checkpoint state while applying runtime safety controls.

Run it through npx with Node.js 20 or newer; the repository documents `npx @putervision/behavior-mcp init` for workspace initialization and `npx @putervision/behavior-mcp run` to start the MCP server. Use the `doctor` and `inspect` commands for diagnostics and runtime inspection.

Yes, the README provides Cursor configuration using `.cursor/mcp.json`. Define a server named `behavior-mcp`, set its command to `behavior-mcp`, pass `run` as the argument, and provide `PENTAD_HMAC_SECRET` in the environment.

Set `PENTAD_HMAC_SECRET` to the secure shared secret used to verify signed intention dispatch tokens.

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