🌽 CornMCP

The AI Agent Intelligence Platform — MCP Server + Analytics Dashboard

Surgical code intelligence. Semantic memory. Quality enforcement. Stop burning tokens on full-file reads. Start extracting exactly what your agent needs.

What is CornMCP?

Corn Hub is a Model Context Protocol (MCP) server and real-time analytics dashboard that gives AI coding agents (Antigravity, Cursor, Claude Code, Codex) surgical access to your codebase through 18 specialized tools.

Instead of dumping entire files into the context window, Corn Hub provides:

🧠 Semantic Memory — Agents remember across sessions via vector search
🔍 Native AST Engine — Real call graphs, type hierarchies, and symbol-level analysis via TypeScript Compiler API
📋 Quality Gates — Plans must score ≥80% before execution
📊 Live Analytics — Track every tool call, latency, and token savings
🔄 Multi-Agent Awareness — Agents see each other's changes in real-time
💾 Zero External Dependencies — No Docker, no external services. Everything runs locally with SQLite.

Architecture

1. System Overview

How the three services connect and communicate.

graph TB
    subgraph IDE["🖥️ Your IDE"]
        Agent["🤖 AI Agent<br/>(Antigravity / Cursor / Claude)"]
    end

    subgraph MCP["🌽 corn-mcp :8317"]
        direction TB
        Transport["Streamable HTTP Transport<br/>/mcp endpoint"]
        Auth["API Key Auth<br/>middleware/auth.ts"]
        Tools["18 MCP Tools"]
        Telemetry["Telemetry Interceptor<br/>Logs every tool call"]

        subgraph ToolGroups["Tool Modules"]
            Memory["🧠 memory.ts<br/>store / search"]
            Knowledge["📚 knowledge.ts<br/>store / search"]
            Code["🔍 code.ts<br/>search / read / context<br/>impact / cypher"]
            Quality["📋 quality.ts<br/>plan_quality / report"]
            Session["🔄 session.ts<br/>start / end"]
            Analytics["📊 analytics.ts<br/>tool_stats"]
            Changes["🔀 changes.ts<br/>changes / detect"]
            Health["💚 health.ts<br/>health / list_repos"]
        end
    end

    subgraph API["⚡ corn-api :4000"]
        direction TB
        Hono["Hono REST API"]
        Routes["15 Route Modules"]
        AST["Native AST Engine<br/>TypeScript Compiler API"]
        SQLite[("💾 SQLite<br/>corn.db")]
        VectorDB[("🧲 SQLite Vector Store<br/>mem9-vectors.db")]
    end

    subgraph Dashboard["📊 corn-web :3000"]
        NextJS["Next.js 16 Dashboard<br/>Turbopack"]
    end

    subgraph Embeddings["☁️ External (Optional)"]
        Voyage["Voyage AI API<br/>voyage-code-3"]
    end

    Agent -->|"JSON-RPC over HTTP<br/>Bearer Token"| Transport
    Transport --> Auth
    Auth --> Tools
    Tools --> Telemetry
    Telemetry -->|"POST /api/metrics/query-log"| Hono

    Memory -->|"Embeds + stores vectors"| VectorDB
    Memory -.->|"Optional: Voyage AI"| Voyage
    Knowledge -->|"Embeds + stores vectors"| VectorDB
    Code -->|"POST /api/intel/*"| Hono
    Quality -->|"POST /api/quality/*"| Hono
    Session -->|"POST /api/sessions/*"| Hono
    Analytics -->|"GET /api/analytics/*"| Hono
    Changes -->|"POST /api/intel/detect-changes"| Hono
    Health -->|"GET /health"| Hono

    Hono --> Routes
    Routes --> AST
    Routes --> SQLite
    AST --> SQLite
    NextJS -->|"fetch() to :4000"| Hono

    style IDE fill:#1a1a2e,stroke:#e94560,color:#fff
    style MCP fill:#16213e,stroke:#0f3460,color:#fff
    style API fill:#0f3460,stroke:#533483,color:#fff
    style Dashboard fill:#533483,stroke:#e94560,color:#fff
    style Embeddings fill:#2d2d2d,stroke:#666,color:#aaa

How It Works

Connection	Protocol	Description
IDE → corn-mcp	JSON-RPC over HTTP	AI agent sends tool calls via MCP SDK. Bearer token auth required.
corn-mcp → corn-api	REST HTTP (fetch)	Tool handlers call Dashboard API endpoints for data persistence.
corn-mcp → SQLite vectors	Direct file I/O	Memory and knowledge tools embed content and store vectors locally.
corn-mcp → Voyage AI	HTTPS API	Optional: semantic embeddings for memory/knowledge search. Falls back to local hash if unavailable.
corn-api → SQLite	sql.js (in-process)	All data stored in `./data/corn.db` — sessions, quality, code graph, analytics.
corn-web → corn-api	REST HTTP (fetch)	Dashboard fetches data from `:4000` API for real-time visualization.
Telemetry	Fire-and-forget POST	Every tool call logs to `/api/metrics/query-log` for analytics dashboard.

2. MCP Request Lifecycle

What happens step-by-step when your AI agent calls a Corn Hub tool.

sequenceDiagram
    participant Agent as 🤖 AI Agent
    participant IDE as 🖥️ IDE (MCP Client)
    participant MCP as 🌽 corn-mcp
    participant Auth as 🔐 Auth Middleware
    participant Tool as 🔧 Tool Handler
    participant API as ⚡ corn-api
    participant DB as 💾 SQLite
    participant Vec as 🧲 Vector Store
    participant Embed as ☁️ Embeddings

    Note over Agent,IDE: Agent decides to call corn_memory_store

    Agent->>IDE: tools/call {name: "corn_memory_store", params: {...}}
    IDE->>MCP: POST /mcp (JSON-RPC, Bearer token)

    MCP->>Auth: validateApiKey(request)
    Auth-->>MCP: ✅ valid (agentId: "local-ide")

    MCP->>MCP: createMcpServer(env)
    MCP->>MCP: new WebStandardStreamableHTTPServerTransport()
    MCP->>MCP: mcpServer.connect(transport)

    MCP->>Tool: corn_memory_store({content, projectId, tags})

    Tool->>Embed: embed(["memory content"])
    alt Voyage AI available
        Embed-->>Tool: [0.12, -0.34, 0.56, ...] (1024 dims)
    else Fallback
        Embed-->>Tool: [hash-based vector] (256 dims)
    end

    Tool->>Vec: INSERT INTO vectors (id, embedding, payload)
    Vec-->>Tool: ✅ stored

    Tool-->>MCP: {content: [{type: "text", text: "✅ Memory stored (id: mem-abc123)"}]}

    MCP->>API: POST /api/metrics/query-log (fire-and-forget telemetry)
    Note over API: Logs: tool=corn_memory_store, status=ok, latency=401ms

    MCP-->>IDE: JSON-RPC response
    IDE-->>Agent: Tool result displayed

    Note over Agent: Agent continues with stored memory context

Key Details

Stateless: Each request creates a fresh McpServer instance — no session state on the server
Auth: Bearer token validated against API keys stored in corn-api database
Telemetry: Every tool call is logged (tool name, status, latency, input size) as fire-and-forget
Embedding Fallback: If Voyage AI is rate-limited or unavailable, auto-rotates models then falls back to local hash embeddings
Model Rotation: voyage-code-3 → voyage-4-large → voyage-4 → voyage-code-2 → voyage-4-lite

3. The 18 MCP Tools — Categories & Data Flow

Every tool grouped by category, showing what backend each one hits.

graph LR
    subgraph Agent["🤖 AI Agent"]
        Call["Tool Call"]
    end

    subgraph Memory["🧠 Memory (2 tools)"]
        MS["corn_memory_store"]
        MR["corn_memory_search"]
    end

    subgraph Knowledge["📚 Knowledge (2 tools)"]
        KS["corn_knowledge_store"]
        KR["corn_knowledge_search"]
    end

    subgraph CodeIntel["🔍 Code Intelligence (7 tools)"]
        CS["corn_code_search"]
        CR["corn_code_read"]
        CC["corn_code_context"]
        CI["corn_code_impact"]
        CY["corn_cypher"]
        LR2["corn_list_repos"]
        DC["corn_detect_changes"]
    end

    subgraph QualityGates["📋 Quality (2 tools)"]
        PQ["corn_plan_quality"]
        QR2["corn_quality_report"]
    end

    subgraph Sessions["🔄 Sessions (2 tools)"]
        SS["corn_session_start"]
        SE["corn_session_end"]
    end

    subgraph Analytics2["📊 Analytics (2 tools)"]
        TS["corn_tool_stats"]
        CH["corn_changes"]
    end

    subgraph Health2["💚 Health (1 tool)"]
        HE["corn_health"]
    end

    subgraph Backends["Backends"]
        VecDB[("🧲 Vector Store<br/>SQLite")]
        APIDB[("💾 corn-api<br/>SQLite")]
        AST2["🏗️ AST Engine"]
        Git["📂 Git CLI"]
    end

    Call --> Memory & Knowledge & CodeIntel & QualityGates & Sessions & Analytics2 & Health2

    MS & MR --> VecDB
    KS & KR --> VecDB
    CS & CC & CI & CY & LR2 --> APIDB
    CS & CC & CI --> AST2
    CR --> Git
    DC --> Git
    DC --> AST2
    PQ & QR2 --> APIDB
    SS & SE --> APIDB
    TS --> APIDB
    CH --> APIDB
    HE --> APIDB

    style Agent fill:#e94560,stroke:#e94560,color:#fff
    style Memory fill:#1a1a2e,stroke:#0f3460,color:#fff
    style Knowledge fill:#1a1a2e,stroke:#0f3460,color:#fff
    style CodeIntel fill:#16213e,stroke:#533483,color:#fff
    style QualityGates fill:#0f3460,stroke:#e94560,color:#fff
    style Sessions fill:#533483,stroke:#e94560,color:#fff
    style Analytics2 fill:#2d2d2d,stroke:#666,color:#fff
    style Health2 fill:#1b4332,stroke:#2d6a4f,color:#fff

Tool Reference

#	Tool	Category	Backend	Description
1	`corn_health`	Health	API	System health — services, uptime, version
2	`corn_list_repos`	Code	API + AST	List indexed repositories with symbol counts
3	`corn_code_search`	Code	AST → SQLite	Hybrid vector/AST symbol search
4	`corn_code_read`	Code	Filesystem	Read raw source code from indexed repos
5	`corn_code_context`	Code	AST → SQLite	360° symbol view: callers, callees, hierarchy
6	`corn_code_impact`	Code	AST → SQLite	Blast radius analysis with recursive CTE
7	`corn_cypher`	Code	AST → SQLite	Cypher-to-SQL graph queries
8	`corn_detect_changes`	Code	Git + AST	Uncommitted changes cross-referenced with graph
9	`corn_memory_store`	Memory	Vector DB	Store agent memory with embedding
10	`corn_memory_search`	Memory	Vector DB	Semantic similarity search over memories
11	`corn_knowledge_store`	Knowledge	Vector DB	Store shared knowledge item
12	`corn_knowledge_search`	Knowledge	Vector DB	Semantic search over knowledge base
13	`corn_plan_quality`	Quality	API SQLite	Score a plan against 8 criteria (must ≥80%)
14	`corn_quality_report`	Quality	API SQLite	Submit 4-dimension quality report (must ≥80%)
15	`corn_session_start`	Session	API SQLite	Begin tracked work session
16	`corn_session_end`	Session	API SQLite	End session with summary and decisions
17	`corn_tool_stats`	Analytics	API SQLite	View tool usage analytics and trends
18	`corn_changes`	Analytics	API SQLite	Check for recent commits by other agents

4. Data Flow — From Code to Intelligence

How your codebase becomes a queryable knowledge graph.

flowchart TD
    subgraph Input["📂 Your Codebase"]
        Files["*.ts, *.tsx, *.js, *.jsx files"]
    end

    subgraph Indexing["🏗️ AST Engine (ast-engine.ts)"]
        Collect["collectFiles()<br/>Recursively scan project<br/>Skip: node_modules, dist, .git"]
        Parse["ts.createProgram()<br/>TypeScript Compiler API<br/>Full type-aware parsing"]
        Extract["AST Walk<br/>Extract symbols + edges"]
        Store["SQLite Persist<br/>INSERT into code_symbols<br/>INSERT into code_edges"]
    end

    subgraph Symbols["📊 Extracted Data"]
        SymTable["code_symbols table<br/>965 rows"]
        EdgeTable["code_edges table<br/>407 rows"]
    end

    subgraph Queries["🔍 Query Layer (intel.ts)"]
        Search["searchSymbols()<br/>LIKE + ranking"]
        Context["getSymbolContext()<br/>JOIN callers/callees/imports"]
        Impact["getSymbolImpact()<br/>Recursive CTE traversal"]
        Cypher["executeCypher()<br/>Cypher → SQL translation"]
    end

    subgraph MCPTools["🌽 MCP Tool Surface"]
        T1["corn_code_search"]
        T2["corn_code_context"]
        T3["corn_code_impact"]
        T4["corn_cypher"]
    end

    subgraph AgentUse["🤖 Agent Uses"]
        Understand["Understand codebase<br/>architecture"]
        PlanChanges["Plan changes with<br/>blast radius awareness"]
        Navigate["Navigate call graphs<br/>and dependencies"]
        Refactor["Safe refactoring with<br/>impact analysis"]
    end

    Files --> Collect --> Parse --> Extract --> Store
    Store --> SymTable & EdgeTable
    SymTable & EdgeTable --> Search & Context & Impact & Cypher
    Search --> T1
    Context --> T2
    Impact --> T3
    Cypher --> T4
    T1 --> Understand
    T2 --> Navigate
    T3 --> PlanChanges
    T4 --> Refactor

    style Input fill:#1a1a2e,stroke:#e94560,color:#fff
    style Indexing fill:#16213e,stroke:#0f3460,color:#fff
    style Symbols fill:#0f3460,stroke:#533483,color:#fff
    style Queries fill:#533483,stroke:#e94560,color:#fff
    style MCPTools fill:#e94560,stroke:#fff,color:#fff
    style AgentUse fill:#2d6a4f,stroke:#40916c,color:#fff

Symbol Extraction Details

Symbol Kind	Example	What's Captured
Function	`createLogger()`	Name, params, return type, file, lines, exported?, signature
Class	`CornError`	Name, extends, implements, methods, properties
Interface	`Logger`	Name, members, extends
Type	`LogLevel`	Name, definition
Enum	`HttpStatus`	Name, members
Variable	`app`	Name, type annotation, initializer

Edge Types

Edge	Meaning	Example
`calls`	Function A calls Function B	`start()` → `getDb()`
`imports`	File A imports from File B	`index.ts` → `@corn/shared-utils`
`extends`	Class A extends Class B	`NotFoundError` → `CornError`
`implements`	Class implements Interface	`LocalHashEmbeddingProvider` → `EmbeddingProvider`

Performance: Corn Hub indexes itself (49 files) in ~2 seconds, extracting 965 symbols and 407 edges.

Project Structure

corn-hub/
├── apps/
│   ├── corn-api/              # Dashboard REST API (Hono + SQLite)
│   │   └── src/
│   │       ├── index.ts       # Server entry, health checks
│   │       ├── db/
│   │       │   ├── client.ts  # SQLite client (sql.js)
│   │       │   └── schema.sql # Database schema (code_symbols, code_edges, etc.)
│   │       ├── services/
│   │       │   └── ast-engine.ts  # 🆕 Native TypeScript Compiler API AST engine
│   │       └── routes/
│   │           ├── intel.ts      # Code intelligence (search, context, impact, cypher)
│   │           ├── indexing.ts   # Trigger AST analysis for projects
│   │           ├── analytics.ts  # Tool usage analytics
│   │           ├── knowledge.ts  # Knowledge base CRUD
│   │           ├── projects.ts   # Project management
│   │           ├── providers.ts  # LLM provider accounts
│   │           ├── quality.ts    # 4D quality reports
│   │           ├── sessions.ts   # Agent session tracking
│   │           ├── setup.ts      # System info
│   │           ├── stats.ts      # Dashboard metrics
│   │           ├── system.ts     # System metrics (CPU, memory)
│   │           ├── usage.ts      # Token usage tracking
│   │           └── webhooks.ts   # Webhook endpoints
│   │
│   ├── corn-mcp/              # MCP Server (18 tools)
│   │   └── src/
│   │       ├── cli.ts         # STDIO transport + telemetry interceptor
│   │       ├── index.ts       # Server factory + tool registration
│   │       ├── node.ts        # HTTP transport entry point
│   │       └── tools/
│   │           ├── analytics.ts   # corn_tool_stats
│   │           ├── changes.ts     # corn_changes, corn_detect_changes
│   │           ├── code.ts        # corn_code_search/read/context/impact, corn_cypher
│   │           ├── health.ts      # corn_health
│   │           ├── knowledge.ts   # corn_knowledge_search/store
│   │           ├── memory.ts      # corn_memory_search/store
│   │           ├── quality.ts     # corn_plan_quality, corn_quality_report
│   │           └── sessions.ts    # corn_session_start/end
│   │
│   └── corn-web/              # Analytics Dashboard (Next.js 16)
│       └── src/
│           ├── app/
│           │   ├── page.tsx       # Main dashboard
│           │   ├── quality/       # Quality reports & grade trends
│           │   ├── sessions/      # Agent session history
│           │   ├── usage/         # Token usage analytics
│           │   ├── knowledge/     # Knowledge base viewer
│           │   ├── projects/      # Project management
│           │   ├── installation/  # IDE setup guide
│           │   └── settings/      # Configuration
│           ├── components/
│           │   └── layout/        # Glassmorphic dashboard shell
│           └── lib/
│               └── api.ts         # API client
│
├── packages/
│   ├── shared-mem9/           # Vector DB + Embedding Provider
│   │   └── src/index.ts       # SQLite vector store, model rotation,
│   │                          # OpenAI/Voyage embeddings, hash fallback
│   ├── shared-types/          # Shared TypeScript interfaces
│   └── shared-utils/          # Logger, ID gen, error classes
│
├── infra/
│   ├── docker-compose.yml     # Optional Docker stack
│   ├── Dockerfile.corn-api
│   ├── Dockerfile.corn-mcp
│   ├── Dockerfile.corn-web
│   └── nginx-dashboard.conf
│
└── .agent/
    └── workflows/
        └── corn-quality-gates.md  # Mandatory AI quality workflow

Native AST Engine

Corn Hub includes a built-in TypeScript Compiler API engine that provides real code intelligence — no external services needed.

What It Extracts

Category	Details
Symbols	Functions, classes, interfaces, types, enums, variables, methods, properties
Edges	Function calls, imports, extends, implements
Metadata	File paths, line ranges, export status, signatures, JSDoc comments

How It Works

Local Project Directory
        │
        ▼
  collectFiles()          Recursively find all .ts/.tsx/.js/.jsx files
        │                 (skips node_modules, dist, .git, etc.)
        ▼
  ts.createProgram()      TypeScript Compiler API parses all files
        │
        ▼
  AST Walk                Extract symbols + build dependency edges
        │
        ▼
  SQLite Storage          INSERT into code_symbols + code_edges tables
        │
        ▼
  Query Functions         searchSymbols, getSymbolContext, getSymbolImpact,
                          executeCypher, getProjectStats

Capabilities

Call Graph: Who calls dbRun? → 8 callers across 5 files with exact line numbers
Impact Analysis: What breaks if I change createLogger? → Recursive CTE traces 6 downstream symbols
Cypher Queries: MATCH (n:class) RETURN n → Finds all 10 classes in the codebase
Import Mapping: Which files import from shared-utils? → Full dependency graph
Type Hierarchies: CornError → NotFoundError, UnauthorizedError, ValidationError

Self-Indexing Results (Corn Hub itself)

Metric	Value
Files Analyzed	49
Symbols Extracted	965
Edges Built	407
By Kind	764 variables, 111 functions, 32 interfaces, 28 methods, 16 properties, 10 classes, 1 type
Analysis Time	~2 seconds

MCP Tools Reference

Corn Hub exposes 18 tools via the Model Context Protocol:

🧠 Memory & Knowledge

Tool	Description
`corn_memory_store`	Store a memory for cross-session recall
`corn_memory_search`	Semantic search across all agent memories
`corn_knowledge_store`	Save reusable patterns, decisions, bug fixes
`corn_knowledge_search`	Search the shared knowledge base

🔍 Code Intelligence (AST-Powered)

Tool	Description
`corn_code_search`	Hybrid vector + AST search across the codebase
`corn_code_read`	Read raw source from indexed repos with line ranges
`corn_code_context`	360° view of a symbol: callers, callees, imports, hierarchy
`corn_code_impact`	Blast radius analysis — recursive CTE on dependency edges
`corn_cypher`	Cypher-like queries translated to SQL against the code graph
`corn_detect_changes`	Analyze uncommitted changes + cross-reference with indexed symbols
`corn_list_repos`	List all indexed repositories with symbol/edge counts

📋 Quality & Sessions

Tool	Description
`corn_plan_quality`	Score a plan against 8 criteria (must pass ≥80%)
`corn_quality_report`	Submit 4D quality scores (Build, Regression, Standards, Traceability)
`corn_session_start`	Begin a tracked work session
`corn_session_end`	End session with summary, files changed, decisions
`corn_changes`	Check for recent changes from other agents

📊 Analytics & System

Tool	Description
`corn_tool_stats`	View tool usage analytics, success rates, latency
`corn_health`	System health check — all services, embedding status

Mandatory Quality Workflow

Every task follows this enforced pipeline:

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  PHASE 0     │────▶│  PHASE 1     │────▶│  PHASE 2     │
│  Session     │     │  Planning    │     │  Execution   │
│  Start       │     │              │     │              │
│              │     │  Plan must   │     │  Build &     │
│ • tool_stats │     │  score ≥80%  │     │  implement   │
│ • changes    │     │  or REJECTED │     │              │
│ • memory     │     │              │     │              │
└──────────────┘     └──────────────┘     └──────┬───────┘
                                                  │
┌──────────────┐     ┌──────────────┐             │
│  PHASE 4     │◀────│  PHASE 3     │◀────────────┘
│  Session     │     │  Quality     │
│  End         │     │  Report      │
│              │     │              │
│ • knowledge  │     │  Score must  │
│ • memory     │     │  be ≥80/100  │
│ • end        │     │  or FAIL     │
│ • stats      │     │              │
└──────────────┘     └──────────────┘

Configure in .agent/workflows/corn-quality-gates.md.

Installation

Prerequisites

Node.js 22+
pnpm 10+
Docker (required for running the full stack)

Quick Start

# Clone
git clone https://github.com/yuki-20/corn-hub.git
cd corn-hub

# Install dependencies
pnpm install

# Start the API backend
cd apps/corn-api && npx tsx src/index.ts

# In another terminal — start the MCP server (HTTP mode)
cd apps/corn-mcp && npx tsx src/node.ts

# In another terminal — start the dashboard
cd apps/corn-web && npx next dev

Service	Port	Description
corn-api	`:4000`	Hono REST API + SQLite + AST Engine
corn-mcp	`:8317`	MCP Gateway (HTTP transport)
corn-web	`:3000`	Next.js Dashboard

IDE Configuration

⚠️ Replace the path below with where YOU cloned corn-hub.

Antigravity / Codex (VS Code)

{
  "mcpServers": {
    "corn": {
      "command": "node",
      "args": ["/path/to/corn-hub/apps/corn-mcp/dist/cli.js"]
    }
  }
}

Cursor

Settings → Features → MCP
Click + Add new MCP server
Name: corn · Type: command
Command: node /path/to/corn-hub/apps/corn-mcp/dist/cli.js

Claude Code

claude mcp add corn -- node /path/to/corn-hub/apps/corn-mcp/dist/cli.js

OS	Example Path
Windows	`C:\Users\You\corn-hub\apps\corn-mcp\dist\cli.js`
macOS	`/Users/You/corn-hub/apps/corn-mcp/dist/cli.js`
Linux	`/home/You/corn-hub/apps/corn-mcp/dist/cli.js`

Indexing Your Project

Once the API is running, create a project and index it:

# Create a project pointing to your local repo
curl -X POST http://localhost:4000/api/projects \
  -H "Content-Type: application/json" \
  -d '{"name":"My Project","gitRepoUrl":"/path/to/your/repo"}'

# Trigger AST analysis (returns projectId from above)
curl -X POST http://localhost:4000/api/intel/analyze \
  -H "Content-Type: application/json" \
  -d '{"projectId":"proj-XXXXX"}'

Dashboard (Optional Docker)

docker compose -f infra/docker-compose.yml up -d --build

Open http://localhost:3000 → the Corn Hub Analytics Dashboard.

Note: Docker is optional. All services run natively with Node.js for development.

Environment Variables

Variable	Default	Description
`OPENAI_API_KEY`	—	Voyage AI / OpenAI API key for embeddings
`OPENAI_API_BASE`	`https://api.voyageai.com/v1`	Embedding API base URL
`MEM9_EMBEDDING_MODEL`	`voyage-code-3`	Primary embedding model
`MEM9_EMBEDDING_DIMS`	`1024`	Embedding dimensions
`MEM9_FALLBACK_MODELS`	`voyage-4-large,voyage-4,voyage-code-2,voyage-4-lite`	Fallback model rotation chain
`DASHBOARD_API_URL`	`http://localhost:4000`	Dashboard API URL

Model Rotation

When the primary model hits rate limits (429), Corn Hub automatically rotates through fallback models:

voyage-code-3 → voyage-4-large → voyage-4 → voyage-code-2 → voyage-4-lite
 (best code)     (largest gen)    (gen-4)    (older code)    (lightweight)

Each model gets 3 retries with exponential backoff before rotating. Set MEM9_FALLBACK_MODELS to customize.

Real Token Savings (Measured Data)

These numbers are from actual usage, not theoretical projections.

During a live 29-call session on the Corn Hub codebase (55 files, 217 KB):

Metric	Value
Avg tokens per tool call	137 tokens
Avg tokens per file read (standard)	~1,500 tokens
Tool call overhead (29 calls)	3,966 tokens
File re-reads prevented	~34,600 tokens saved

Savings by Codebase Size

Repo Size	Standard Agent	With Corn Hub	Savings
Small (55 files)	~195K tokens	~135K tokens	30%
Medium (200 files)	~450K tokens	~180K tokens	60%
Large (1000 files)	~1.2M tokens	~250K tokens	79%
Enterprise (5000+)	~3M+ tokens	~400K tokens	87%

Corn Hub's semantic search is O(1) — it returns ~137 tokens regardless of codebase size.

Troubleshooting

Error: Cannot find module '.../dist/cli.js'

Run pnpm build first — the dist/ folder is generated by the build step
Check the path points to YOUR local clone
On Windows, use forward slashes or escaped backslashes in JSON config

429 Too Many Requests from Voyage AI

Free tier: 3 RPM, 10K TPM. Corn Hub automatically retries with backoff and rotates models
Add a payment method at dashboard.voyageai.com for higher limits

Dashboard shows 0 agents / 0 queries

Restart your IDE to reload the MCP server with the latest telemetry interceptor
Ensure corn-api is running on port 4000

STDIO invalid character '\x1b' errors

Corn Hub patches console.log to redirect to stderr. If a dependency bypasses this, check for ANSI color output leaking to stdout.

Code intelligence returns empty results

Ensure your project is indexed: POST /api/intel/analyze {"projectId":"..."}
Check the project has gitRepoUrl set to a valid local directory path

Tech Stack

Layer	Technology	Why
MCP Server	TypeScript + `@modelcontextprotocol/sdk`	Type-safe tool definitions
API	Hono 4	Ultra-fast, 0-dependency HTTP
Database	sql.js (WASM SQLite)	In-memory + file persistence, no C++ deps
AST Engine	TypeScript Compiler API	Real call graphs, not text grep
Vectors	SQLite vector store	Cosine similarity search, no external DB
Embeddings	Voyage AI (voyage-code-3)	Best-in-class code retrieval
Dashboard	Next.js 16 (Turbopack)	Fast dev, modern React
Monorepo	pnpm + Turborepo	Incremental builds

Releases

See the GitHub Releases page for version history and changelogs.

Contributors

yuki-20	AntiTamper
🏆 Creator & Lead	🤝 Co-Contributor

License

MIT © yuki-20

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
.agent/workflows		.agent/workflows
apps		apps
infra		infra
packages		packages
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
pnpm-workspace.yaml		pnpm-workspace.yaml
start.cmd		start.cmd
start.sh		start.sh
tsconfig.json		tsconfig.json
turbo.json		turbo.json
version.json		version.json

Folders and files

Latest commit

History

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🌽 CornMCP

What is CornMCP?

Architecture

1. System Overview

How It Works

2. MCP Request Lifecycle

Key Details

3. The 18 MCP Tools — Categories & Data Flow

Tool Reference

4. Data Flow — From Code to Intelligence

Symbol Extraction Details

Edge Types

Project Structure

Native AST Engine

What It Extracts

How It Works

Capabilities

Self-Indexing Results (Corn Hub itself)

MCP Tools Reference

🧠 Memory & Knowledge

🔍 Code Intelligence (AST-Powered)

📋 Quality & Sessions

📊 Analytics & System

Mandatory Quality Workflow

Installation

Prerequisites

Quick Start

IDE Configuration

Antigravity / Codex (VS Code)

Cursor

Claude Code

Indexing Your Project

Dashboard (Optional Docker)

Environment Variables

Model Rotation

Real Token Savings (Measured Data)

Savings by Codebase Size

Troubleshooting

Tech Stack

Releases

Contributors

License

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