mcp-server

HUMMBL MCP Server – User Guide

1. Purpose & Audience

This guide explains how strategists, operators, and engineers can install, configure, and operate the HUMMBL MCP Server to access the Base120 mental models and Self-Dialectical AI Systems methodology through Model Context Protocol (MCP) clients. Follow these steps to deliver consistent cognition support across Claude Desktop, IDE copilots, and internal tools.

2. Prerequisites

3. Installation Options

Scenario Command Notes
Global install (recommended) npm install -g @hummbl/mcp-server Makes hummbl-mcp CLI available system-wide
On-demand npx @hummbl/mcp-server Pulls latest version without persistent install
Project dependency npm install @hummbl/mcp-server --save-dev Useful when bundling with bespoke tooling

After installation, run hummbl-mcp --help to confirm availability.

4. Client Configuration

4.1 Claude Desktop (macOS & Windows)

  1. Locate the config file:
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%/Claude/claude_desktop_config.json
  2. Add the HUMMBL entry (keep npm/npx consistent with installation):
{
  "mcpServers": {
    "hummbl": {
      "command": "npx",
      "args": ["-y", "@hummbl/mcp-server"]
    }
  }
}
  1. Restart Claude Desktop. HUMMBL tools appear under the attachments menu.

4.2 Perplexity or Other MCP-Aware Copilots

  1. Consult the client’s MCP settings UI.
  2. Set command to hummbl-mcp (or npx -y @hummbl/mcp-server).
  3. Choose stdio transport when asked; no API keys are required today.

4.3 VS Code / JetBrains Extensions

  1. Install an MCP-compatible extension (e.g., Perplexity MCP for VS Code).
  2. Register a new server pointing to hummbl-mcp.
  3. Enable the extension. The HUMMBL tools become callable within the editor command palette.

4.4 Automation & CI Pipelines

Use the CLI directly inside scripts:

hummbl-mcp --transport stdio <<'EOF'
{"method":"hummbl/get_model","params":{"code":"DE3"}}
EOF

This enables batch audits of model references or automated methodology exports.

5. Using HUMMBL Tools (Base120)

Tool Purpose Minimal Params
get_model Fetch a single model with description, example, tags { "code": "P1" }
list_all_models Enumerate the 120 models or filter by transformation { "transformation_filter": "IN" } (optional)
search_models Keyword search across names, descriptions, examples { "query": "decision" }
recommend_models Receive recommendations based on a problem statement { "problem_description": "Scaling ops" }
get_transformation Return all models within a transformation (P/IN/CO/DE/RE/SY) { "type": "SY" }
search_problem_patterns Retrieve HUMMBL problem patterns plus suggested models { "query": "ethics" }
get_methodology Download the Self-Dialectical AI methodology bundle {}
audit_model_references Validate lists of model references in briefs { "items": [{"code": "IN11"}] }

All responses follow the HUMMBL Result pattern with explicit ok/error fields. Handle errors by inspecting error.code and error.message.

6. Resources (URI Access)

Clients that support MCP resources can fetch the canonical JSON/Markdown directly without invoking tools.

7. Troubleshooting Checklist

  1. Server will not start – ensure Node.js 20+, run npm install, then retry npx @hummbl/mcp-server.
  2. Claude does not list HUMMBL tools – confirm config JSON syntax, remove trailing commas, restart Claude Desktop.
  3. Tool errors referencing missing models – run get_model with the exact code (e.g., DE3). The audit tool flags typos.
  4. Network restrictions – preload the package (npm pack @hummbl/mcp-server) inside restricted environments.
  5. Review TROUBLESHOOTING.md for deeper diagnostics and support escalation paths.

8. Operational Best Practices

9. Support & Feedback

Stay aligned with HUMMBL Base120 principles and cite transformation codes in prompts for maximum fidelity.