nodus-mcp-server 0.1.7

MCP server powered by the Nodus orchestration runtime

nodus-mcp-server

An MCP (Model Context Protocol) server that exposes the Nodus orchestration runtime as tools for Claude Desktop and other MCP-compatible hosts.

What it does

Six tools over a single server process:

Tool	Description
nodus.remember	Store a fact in persistent SQLite memory with optional tags
nodus.recall	Search memory by free-text query and/or tags
nodus.forget	Delete a memory entry by ID
nodus.run_goal	Run a Nodus goal (sandboxed, structured step results)
nodus.run_workflow	Run a Nodus workflow (checkpoint/resume capable)
nodus.exec	Execute arbitrary .nd code (10 s timeout, no file I/O)

Requirements

• Python ≥ 3.10
• nodus-lang >= 4.0.4
• nodus-mcp >= 0.1.0

Install

pip install nodus-lang nodus-mcp nodus-mcp-server

Claude Desktop setup

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "nodus": {
      "command": "nodus-mcp-server",
      "args": ["--stdio"]
    }
  }
}

Restart Claude Desktop. The six nodus.* tools will appear in the tool list.

Memory persists at ~/.nodus-mcp-server/data/memory.db and survives upgrades.

HTTP mode

For remote or multi-client use:

nodus-mcp-server --http --port 8080
nodus-mcp-server --http --port 8080 --bearer-token <secret>

Built-in goals and workflows

Goals

summarize — params: {text: string}

Counts characters and classifies text size (short / medium / long).

{
  "name": "summarize",
  "params": {"text": "Your text here"}
}

Returns:

{
  "steps": {
    "measure": 14,
    "classify": {"chars": 14, "size": "short", "empty": false}
  }
}

pipeline — params: {items: list, label: string}

Validates an item list and produces a labelled report.

{
  "name": "pipeline",
  "params": {"items": [1, 2, 3], "label": "batch-1"}
}

Returns:

{
  "steps": {
    "validate": 3,
    "report": {"label": "batch-1", "item_count": 3, "has_items": true, "status": "complete"}
  }
}

Workflows

research — params: {topic: string}

Two-step planning + execution workflow with checkpoints at each step.

{
  "name": "research",
  "params": {"topic": "LLM context windows"}
}

Returns:

{
  "steps": {
    "plan": {"query": "LLM context windows", "strategy": "step-by-step"},
    "execute": {"topic": "LLM context windows", "query": "LLM context windows", "strategy": "step-by-step", "status": "complete"}
  }
}

Adding your own goals and workflows

Drop a .nd file into goals/ or workflows/. The file should only define the goal or workflow — do not call run_goal() or run_workflow() at the bottom (the server calls it for you).

// goals/my_goal.nd  — input variable injected via params
goal my_goal {
    step process {
        let result = len(input_text)
        return {"length": result, "has_content": result > 0i}
    }
}

Then call it:

{"name": "my_goal", "params": {"input_text": "hello"}}

Sandbox

.nd scripts run with:

• No file system access (allowed_paths=[])
• No network access
• Goal timeout: 30 s
• Workflow timeout: 60 s
• nodus.exec timeout: 10 s

Architecture

server.py          — NodusRuntime, tool registration, MCP transport
runner.py          — goal/workflow execution via ModuleLoader + VM
memory_store.py    — SQLite-backed thread-safe memory store
goals/             — .nd goal definitions
workflows/         — .nd workflow definitions
~/.nodus-mcp-server/data/memory.db  — SQLite DB (persists across upgrades)

License

MIT