agentkit-mcp 0.1.1

AgentKit MCP Server — Business Intelligence tools for Claude/Cursor agents.

AgentKit — MCP Server for Business Intelligence Agents

Expose enterprise KPIs, health scores, forecasting, and anomaly detection as tools, resources, and prompt templates that any MCP-compatible agent (Claude Desktop, Cursor, LangGraph, Claude Agent SDK, CrewAI) can use.

🔗 Live MCP server (SSE): https://agentkit.ysiddo-ai-projects.app/sse — connect from Claude Desktop via mcp-remote (see claude_desktop_config.example.json). On-demand backend (first call ~30–60 s).

What It Does

• 6 MCP Tools: query_kpis, get_company_health, detect_kpi_anomalies, forecast_metric, list_available_metrics, get_executive_summary
• 6 MCP Resources: kpi://Finance/latest and similar for Growth, Operations, People, ESG, IT_Ops
• 1 Reusable Prompt: monthly_executive_briefing
• LangGraph 3-agent workflow in workflow.py
• Claude Agent SDK demo in demos/claude_agent_sdk_demo.py
• CrewAI demo in demos/crewai_demo.py
• DSPy research scaffold in research/dspy_experiment.py

Quick Start

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env  # fill in keys + POSTGRES_URL
python mcp_server.py

Claude Desktop Setup

Add to ~/.config/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "agentkit": {
      "command": "python",
      "args": ["/abs/path/to/agentkit/mcp_server.py"],
      "env": {
        "POSTGRES_URL": "postgresql://...",
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Restart Claude Desktop, then ask:

• "What's our company health right now?"
• "Forecast revenue for the next 6 months."
• "Are there anomalies in the Finance KPIs?"

LangGraph Workflow

from workflow import analyze
result = analyze("What drove gross margin in Q1?")
print(result["report"])

Architecture

        Claude Desktop / Cursor / LangGraph
                      │
                      ▼ MCP
              ┌──────────────────┐
              │  mcp_server.py   │
              │   6 tools        │
              │   6 resources    │
              │   1 prompt       │
              └────────┬─────────┘
                       │
        ┌──────────────┼──────────────┐
        ▼              ▼              ▼
   pg_store      insights      forecasting
   (KPIs)        (health,      (LinearReg
                 anomalies)    + Monte Carlo)

License

MIT

⚖️ License & Enterprise Use (Dual-License)

This project is open-source under the AGPL-3.0 License. It is completely free for researchers, students, and open-source hobbyists.

Commercial Use: The AGPLv3 license requires that any proprietary network service (SaaS, internal corporate tools) that uses or modifies this code must also open-source its entire backend.

If you wish to use this framework in a closed-source commercial environment, or require Enterprise features (SSO, Active Directory, Custom VPC Deployment, Strict RBAC), you must obtain a Commercial License. Please reach out to discuss commercial licensing and integration consulting.

📡 Anonymous Telemetry

This project collects anonymous, GDPR-compliant startup pings to help the author understand usage volume and prioritize development.

• What is collected: Only the project name and a "startup" event timestamp. No PII, no API keys, no user data.
• How to disable: We respect your privacy. To opt-out, simply set TELEMETRY_OPT_OUT=true in your .env file.