Corn Intelligence MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Corn Intelligence MCP ServerShow me the system health"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🌽 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 |
| Core | System health — CPU, RAM, uptime |
2 |
| Core | Begin a tracked work session |
3 |
| Core | End session with summary |
4 |
| Memory | Store agent memory with tags |
5 |
| Memory | Keyword search over memories |
6 |
| Knowledge | Store a shared knowledge item |
7 |
| Knowledge | Semantic search over knowledge base |
8 |
| Code | Read raw source code from any file path |
9 |
| Code | Uncommitted git changes cross-referenced with AST graph |
10 |
| Code | List indexed repositories with symbol counts (auto-indexes on first call) |
11 |
| Code | Hybrid AST symbol search by name |
12 |
| Code | 360° symbol view: callers, callees, hierarchy |
13 |
| Code | Blast radius analysis — which files depend on a given file |
14 |
| Code | Graph-style queries: |
15 |
| Analytics | Usage analytics over last 50 tool calls |
16 |
| Quality | Submit a 3-dimension quality report (Clarity/Efficiency/Security) |
17 |
| Analytics | Log raw conversation token usage to Dashboard |
18 |
| Quality | Score a plan text against 8 quality criteria (must ≥80%) |
19 |
| Analytics | Check recent git commits by agents ( |
3. AST Code Intelligence Engine
Auto-Indexing:
corn_list_reposautomatically scans and indexes Python source files on first call.Symbol Graph: Stores
Function,Class, andMethodnodes withCALLS/INHERITSrelationships in MySQL.Blast Radius:
corn_code_impactidentifies 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%!
Related MCP server: MCP of MCPs
🏛️ 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
Launch Services:
docker-compose up -dConnect 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"] } } }Run Migrations (first-time setup):
docker exec personal-mcp-web python manage.py migrateExplore Dashboard: http://localhost:8000/
🛠️ Development
Apply new migrations after model changes:
docker exec personal-mcp-web python manage.py makemigrations mcp_server
docker exec personal-mcp-web python manage.py migrate mcp_serverCheck Django config inside container:
docker exec personal-mcp-web python manage.py checkView live logs:
docker logs -f personal-mcp-webPowered by Antigravity — Premium AI Engineering.
This server cannot be deployed
Maintenance
Related MCP Connectors
LLM cost optimization gateway, repo token-waste auditor & model arbitrage MCP server.
LLM cost optimization gateway, repo token-waste auditor & model arbitrage MCP server.
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
Related MCP Servers
- FlicenseNot gradedqualityNot gradedmaintenanceA dual-server MCP implementation with task-based AI processing using Google Gemini API, featuring tool integration, real-time monitoring dashboard, and extensible framework for custom AI workflows.-
- AlicenseNot gradedqualityDmaintenanceA meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.16 npm10Apache 2.0
- AlicenseNot gradedqualityDmaintenanceAn MCP server that provides structure-aware code analysis (symbol trees, dependencies, docs) to reduce AI agent token consumption by up to 99%, along with Git commit intelligence.MIT
- FlicenseNot gradedqualityDmaintenanceA versatile MCP server that enables natural language software development tasks using multiple LLM providers (OpenAI, Anthropic) with real-time visualization, cost management, and a comprehensive tool suite.-