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 (dashboard): https://agentkit.ysiddo-ai-projects.app — connect from Claude Desktop via mcp-remote (see claude_desktop_config.example.json). On-demand backend (first call ~30–60 s). Self-hosting: see SELF_HOSTING.md.

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 (Planner → Analyst → Reporter)
• 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
• 34 tests across smoke, API, integration, and LangGraph workflow

PyPI Package

pip install agentkit-mcp   # v0.1.4
agentkit-mcp               # CLI entrypoint

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-...",
        "GROQ_API_KEY": "gsk_...",
        "OPENAI_API_KEY": "sk-...",
        "LLM_DEFAULT": "groq/llama-3.3-70b-versatile",
        "LLM_REASONING": "anthropic/claude-sonnet-4-6",
        "LLM_JUDGE": "anthropic/claude-haiku-4-5",
        "LLM_LOCAL": "ollama/llama3.3",
        "LOG_LEVEL": "DEBUG",
        "TELEMETRY_OPT_OUT": "true"
      }
    }
  }
}

Leveraging Full Platform Capabilities

AgentKit is highly configurable. Make sure you are not underestimating its capabilities by omitting key environment variables:

• LLM Routing/Overrides: Use LLM_DEFAULT, LLM_REASONING, LLM_JUDGE, and LLM_LOCAL to precisely route distinct tasks to the most suitable models, ensuring you get the best balance of speed and cost.
• Provider Support: In addition to Anthropic and Groq, OpenAI (OPENAI_API_KEY) and Ollama are natively supported.
• Diagnostics: You can adjust LOG_LEVEL to DEBUG to gain deeper insights into the orchestration engine.
• Telemetry: The platform automatically sends anonymous telemetry, but you have the flexibility to disable it via TELEMETRY_OPT_OUT=true.

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 agentkit_mcp.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)

Research Novelty & Scientific Contributions

AgentKit is both industry-proof and scientifically reproducible:

• Standardized Model Context Protocol (MCP) Middleware: Unified stdio and SSE transport for hot-swappable agent tools.
• Zero-Latency Schema Validation: Formal runtime schema type checking and injection safety bounds.
• Multi-Agent Interoperability: Tested and verified across Claude Desktop, Cursor IDE, and Devin AI.

For full theoretical formulation, math bounds, and citation details, see RESEARCH.md.

Benchmark Replication Suite

Run the reproducible benchmark evaluation with a fixed random seed:

python3 eval/run_benchmarks.py --seed 42

Integration Guides (Claude Desktop, Cursor, Devin)

• Claude Desktop: See claude_desktop_config.example.json and docs/INTEGRATION_GUIDE.md
• Cursor IDE: See cursor_mcp.example.json
• Devin AI Agent: See devin_mcp.example.json

Automated client verification:

python3 tests/test_mcp_client.py

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 license: see COMMERCIAL.md.