Corn Intelligence MCP Server

🌽 Personal MCP Intelligence Platform v2.0

A high-performance Model Context Protocol (MCP) server integrated with a Django-powered analytics dashboard. Features the Corn Intelligence toolset for comprehensive AI interaction tracking, AST code analysis, and real-time token efficiency metrics.

🚀 Key Features

1. Intelligence Dashboard

• Real-time Monitoring: Every tool call is logged with latency, token usage, and efficiency metrics.
• Auto-Reload: Dashboard refreshes every 15 seconds with a visual countdown indicator.
• Token Analytics: Visualize direct token costs vs. context savings with efficiency scoring.
• Session Explorer: Browse and drill into past sessions at /sessions/.

2. Corn Intelligence Toolset (v2.0) — 19 Tools

| # | Tool | Category | Description | |---|---|---|---| | 1 | corn_health | Core | System health — CPU, RAM, uptime | | 2 | corn_session_start | Core | Begin a tracked work session | | 3 | corn_session_end | Core | End session with summary | | 4 | corn_memory_store | Memory | Store agent memory with tags | | 5 | corn_memory_search | Memory | Keyword search over memories | | 6 | corn_knowledge_store | Knowledge | Store a shared knowledge item | | 7 | corn_knowledge_search | Knowledge | Semantic search over knowledge base | | 8 | corn_code_read | Code | Read raw source code from any file path | | 9 | corn_detect_changes | Code | Uncommitted git changes cross-referenced with AST graph | | 10 | corn_list_repos | Code | List indexed repositories with symbol counts (auto-indexes on first call) | | 11 | corn_code_search | Code | Hybrid AST symbol search by name | | 12 | corn_code_context | Code | 360° symbol view: callers, callees, hierarchy | | 13 | corn_code_impact | Code | Blast radius analysis — which files depend on a given file | | 14 | corn_cypher | Code | Graph-style queries: (a)-[:CALLS]->(b) | | 15 | corn_tool_stats | Analytics | Usage analytics over last 50 tool calls | | 16 | corn_quality_report | Quality | Submit a 3-dimension quality report (Clarity/Efficiency/Security) | | 17 | corn_record_conversation | Analytics | Log raw conversation token usage to Dashboard | | 18 | corn_plan_quality | Quality | Score a plan text against 8 quality criteria (must ≥80%) | | 19 | corn_changes | Analytics | Check recent git commits by agents (git log -n 5) |

3. AST Code Intelligence Engine

• Auto-Indexing: corn_list_repos automatically scans and indexes Python source files on first call.
• Symbol Graph: Stores Function, Class, and Method nodes with CALLS / INHERITS relationships in MySQL.
• Blast Radius: corn_code_impact identifies all callers transitively affected by changes to a file.

4. Token Efficiency Metrics

• Accurate Counting: Heuristic blends character density and word count — max(chars/4, words × 1.35) — for reliable estimation across code and natural language.
• Context Saved: For search tools (memory_search, knowledge_search, code_search), tokens saved = total DB tokens − tokens returned. Reflects real context reduction.
• Efficiency (%): Saved / (Used + Saved). Aim for >80%!

🏛️ Architecture

IDE (Antigravity / Claude)
    │  stdio (MCP protocol)
    ▼
Docker: personal-mcp-web
    ├── Django MCP Server (run_mcp management command)
    │       └── 19 Corn Intelligence Tools
    ├── AST Indexer (mcp_server/utils/indexer.py)
    └── Django Dashboard (http://localhost:8000)
            └── Real-time Activity Log, Sessions, Token Analytics
    │
    ▼
Docker: mcp_mysql_db (MySQL 8)
    ├── Session, ToolLog
    ├── Memory, Knowledge
    └── Repository, Symbol, SymbolRelation, PlanQuality

🏃 Getting Started

1. Launch Services:

    docker-compose up -d

1. Connect IDE — add to your MCP config (mcp_config.json):

    {
      "mcpServers": {
        "personal-mcp-v2": {
          "command": "/usr/local/bin/docker",
          "args": ["exec", "-i", "personal-mcp-web", "python", "manage.py", "run_mcp", "--verbosity", "0", "--no-color", "--skip-checks"]
        }
      }
    }

1. Run Migrations (first-time setup):

    docker exec personal-mcp-web python manage.py migrate

1. Explore Dashboard: http://localhost:8000/

🛠️ Development

Apply new migrations after model changes: ``bash docker exec personal-mcp-web python manage.py makemigrations mcp_server docker exec personal-mcp-web python manage.py migrate mcp_server ``

Check Django config inside container: ``bash docker exec personal-mcp-web python manage.py check ``

View live logs: ``bash docker logs -f personal-mcp-web ``

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