iflow-mcp_codealive-mcp 0.4.7
MCP server for the CodeAlive API
Requires Python: >=3.11

# CodeAlive MCP: Deepest Context Engine for your projects (especially for large codebases)

<!-- MCP Server Name: io.github.codealive-ai.codealive-mcp -->

[![CodeAlive Logo](https://app.codealive.ai/images/logos/dark-logo.svg)](https://www.codealive.ai/)

**Connect your AI assistant to CodeAlive's powerful code understanding platform in seconds!**

This MCP (Model Context Protocol) server enables AI clients like Claude Code, Cursor, Claude Desktop, Continue, VS Code (GitHub Copilot), Cline, Codex, OpenCode, Qwen Code, Gemini CLI, Roo Code, Goose, Kilo Code, Windsurf, Kiro, Qoder, n8n, and Amazon Q Developer to access CodeAlive's advanced semantic code search and codebase interaction features.

## What is CodeAlive?

The most accurate and comprehensive Context Engine as a service, optimized for large codebases, powered by advanced GraphRAG and accessible via MCP. It enriches the context for AI agents like Cursor, Claude Code, Codex, etc., making them 35% more efficient and up to 84% faster.

It's like Context7, but for your (large) codebases.

It allows AI-Coding Agents to:

*   **Find relevant code faster** with semantic search
*   **Understand the bigger picture** beyond isolated files  
*   **Provide better answers** with full project context
*   **Reduce costs and time** by removing guesswork

## 🛠 Available Tools

Once connected, you'll have access to these powerful tools:

1. **`get_data_sources`** - List your indexed repositories and workspaces
2. **`codebase_search`** - Semantic code search across your indexed codebase (main/master branch)  
3. **`codebase_consultant`** - AI consultant with full project expertise

## 🎯 Usage Examples

After setup, try these commands with your AI assistant:

- *"Show me all available repositories"* → Uses `get_data_sources`
- *"Find authentication code in the user service"* → Uses `codebase_search`
- *"Explain how the payment flow works in this codebase"* → Uses `codebase_consultant`

## Table of Contents

*   [Quick Start (Remote)](#-quick-start-remote)
*   [AI Client Integrations](#-ai-client-integrations)
*   [Advanced: Local Development](#-advanced-local-development)
*   [Community Plugins](#-community-plugins)
*   [HTTP Deployment (Self-Hosted & Cloud)](#-http-deployment-self-hosted--cloud)
*   [Available Tools](#-available-tools)
*   [Usage Examples](#-usage-examples)
*   [Troubleshooting](#-troubleshooting)
*   [License](#-license)

## 🚀 Quick Start (Remote)

**The fastest way to get started** - no installation required! Our remote MCP server at `https://mcp.codealive.ai/api` provides instant access to CodeAlive's capabilities.

### Step 1: Get Your API Key

1. Sign up at [https://app.codealive.ai/](https://app.codealive.ai/)
2. Navigate to **MCP & API**
3. Click **"+ Create API Key"**
4. Copy your API key immediately - you won't see it again!

### Step 2: Choose Your AI Client

Select your preferred AI client below for instant setup:

## 🚀 Quick Start (Agentic Installation)

You may ask your AI agent to install the CodeAlive MCP server for you.

1. Copy-Paste the following prompt into your AI agent (remember to insert your API key):
```
Here is CodeAlive API key: PASTE_YOUR_API_KEY_HERE

Add the CodeAlive MCP server by following the installation guide from the README at https://raw.githubusercontent.com/CodeAlive-AI/codealive-mcp/main/README.md

Find the section "AI Client Integrations" and locate your client (Claude Code, Cursor, Gemini CLI, etc.). Each client has specific setup instructions:
- For Gemini CLI: Use the one-command setup with `gemini mcp add`
- For Claude Code: Use `claude mcp add` with the --transport http flag
- For other clients: Follow the configuration snippets provided

Prefer the Remote HTTP option when available. If API key is not provided above, help me issue a CodeAlive API key first.
```
Then allow execution.

2. Restart your AI agent.

## 🤖 AI Client Integrations

<details>
<summary><b>Claude Code</b></summary>

**Option 1: Remote HTTP (Recommended)**

```bash
claude mcp add --transport http codealive https://mcp.codealive.ai/api --header "Authorization: Bearer YOUR_API_KEY_HERE"
```

**Option 2: Docker (STDIO)**

```bash
claude mcp add codealive-docker /usr/bin/docker run --rm -i -e CODEALIVE_API_KEY=YOUR_API_KEY_HERE ghcr.io/codealive-ai/codealive-mcp:v0.3.0
```

Replace `YOUR_API_KEY_HERE` with your actual API key.

</details>

<details>
<summary><b>Cursor</b></summary>

**Option 1: Remote HTTP (Recommended)**

1. Open Cursor → Settings (`Cmd+,` or `Ctrl+,`)
2. Navigate to **"MCP"** in the left panel
3. Click **"Add new MCP server"**
4. Paste this configuration:

```json
{
  "mcpServers": {
    "codealive": {
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

5. Save and restart Cursor

**Option 2: Docker (STDIO)**

```json
{
  "mcpServers": {
    "codealive": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>Codex</b></summary>

OpenAI Codex CLI supports MCP via `~/.codex/config.toml`.

**`~/.codex/config.toml` (Docker stdio – recommended)**
```toml
[mcp_servers.codealive]
command = "docker"
args = ["run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"]
```

**Experimental: Streamable HTTP (requires experimental_use_rmcp_client)**

> **Note:** Streamable HTTP support requires enabling the experimental Rust MCP client in your Codex configuration.

```toml
[mcp_servers.codealive]
url = "https://mcp.codealive.ai/api"
headers = { Authorization = "Bearer YOUR_API_KEY_HERE" }
```

</details>

<details>
<summary><b>Gemini CLI</b></summary>

**One command setup (complete):**

```bash
gemini mcp add --transport http secure-http https://mcp.codealive.ai/api --header "Authorization: Bearer YOUR_API_KEY_HERE"
```

Replace `YOUR_API_KEY_HERE` with your actual API key. That's it - no config files needed! 🎉

</details>

<details>
<summary><b>Continue</b></summary>

**Option 1: Remote HTTP (Recommended)**

1. Create/edit `.continue/config.yaml` in your project or `~/.continue/config.yaml`
2. Add this configuration:

```yaml
mcpServers:
  - name: CodeAlive
    type: streamable-http
    url: https://mcp.codealive.ai/api
    requestOptions:
      headers:
        Authorization: "Bearer YOUR_API_KEY_HERE"
```

3. Restart VS Code

**Option 2: Docker (STDIO)**

```yaml
mcpServers:
  - name: CodeAlive
    type: stdio
    command: docker
    args:
      - run
      - --rm
      - -i
      - -e
      - CODEALIVE_API_KEY=YOUR_API_KEY_HERE
      - ghcr.io/codealive-ai/codealive-mcp:v0.3.0
```

</details>

<details>
<summary><b>Visual Studio Code with GitHub Copilot</b></summary>

**Option 1: Remote HTTP (Recommended)**

> **Note:** VS Code supports both Streamable HTTP and SSE transports, with automatic fallback to SSE if Streamable HTTP fails.

1. Open Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P`)
2. Run **"MCP: Add Server"**
3. Choose **"HTTP"** server type
4. Enter this configuration:

```json
{
  "servers": {
    "codealive": {
      "type": "http",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

5. Restart VS Code

**Option 2: Docker (STDIO)**

Create `.vscode/mcp.json` in your workspace:

```json
{
  "servers": {
    "codealive": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>Claude Desktop</b></summary>

> **Note:** Claude Desktop remote MCP requires OAuth authentication. Use Docker option for Bearer token support.

**Docker (STDIO)**

1. Edit your config file:
   - **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json`
   - **Windows:** `%APPDATA%\Claude\claude_desktop_config.json`

2. Add this configuration:

```json
{
  "mcpServers": {
    "codealive": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

3. Restart Claude Desktop

</details>

<details>
<summary><b>Cline</b></summary>

**Option 1: Remote HTTP (Recommended)**

1. Open Cline extension in VS Code
2. Click the MCP Servers icon to configure
3. Add this configuration to your MCP settings:

```json
{
  "mcpServers": {
    "codealive": {
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

4. Save and restart VS Code

**Option 2: Docker (STDIO)**

```json
{
  "mcpServers": {
    "codealive": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>OpenCode</b></summary>

Add CodeAlive as a **remote** MCP server in your `opencode.json`.

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "codealive": {
      "type": "remote",
      "url": "https://mcp.codealive.ai/api",
      "enabled": true,
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

</details>

<details>
<summary><b>Qwen Code</b></summary>

Qwen Code supports MCP via `mcpServers` in its `settings.json` and multiple transports (stdio/SSE/streamable-http). Use **streamable-http** when available; otherwise use Docker (stdio).

**`~/.qwen/settings.json` (Streamable HTTP)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "streamable-http",
      "url": "https://mcp.codealive.ai/api",
      "requestOptions": {
        "headers": {
          "Authorization": "Bearer YOUR_API_KEY_HERE"
        }
      }
    }
  }
}
```

**Fallback: Docker (stdio)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "--rm", "-i",
               "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
               "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"]
    }
  }
}
```

</details>

<details>
<summary><b>Roo Code</b></summary>

Roo Code reads a JSON settings file similar to Cline.

**Global config:** `mcp_settings.json` (Roo) or `cline_mcp_settings.json` (Cline-style)

**Option A — Remote HTTP**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "streamable-http",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**Option B — Docker (STDIO)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

> **Tip:** If your Roo build doesn't honor HTTP headers, use the Docker/STDIO option.

</details>

<details>
<summary><b>Goose</b></summary>

**UI path:** Settings → MCP Servers → Add → choose Streamable HTTP

**Streamable HTTP configuration:**
- **Name:** `codealive`
- **Endpoint URL:** `https://mcp.codealive.ai/api`
- **Headers:** `Authorization: Bearer YOUR_API_KEY_HERE`

**Docker (STDIO) alternative:**

Add a STDIO extension with:
- **Command:** `docker`
- **Args:** `run --rm -i -e CODEALIVE_API_KEY=YOUR_API_KEY_HERE ghcr.io/codealive-ai/codealive-mcp:v0.3.0`

</details>

<details>
<summary><b>Kilo Code</b></summary>

**UI path:** Manage → Integrations → Model Context Protocol (MCP) → Add Server

**HTTP**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "streamable-http",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**STDIO (Docker)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>Windsurf (Codeium)</b></summary>

**File:** `~/.codeium/windsurf/mcp_config.json`

```json
{
  "mcpServers": {
    "codealive": {
      "type": "streamable-http",
      "serverUrl": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

</details>

<details>
<summary><b>Kiro</b></summary>

> **Note:** Kiro does not yet support remote MCP servers natively. Use the `mcp-remote` workaround to connect to remote HTTP servers.

**Prerequisites:**
```bash
npm install -g mcp-remote
```

**UI path:** Settings → MCP → Add Server

**Global file:** `~/.kiro/settings/mcp.json`
**Workspace file:** `.kiro/settings/mcp.json`

**Remote HTTP (via mcp-remote workaround)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.codealive.ai/api",
        "--header",
        "Authorization: Bearer ${CODEALIVE_API_KEY}"
      ],
      "env": {
        "CODEALIVE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**Docker (STDIO)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>Qoder</b></summary>

**UI path:** User icon → Qoder Settings → MCP → My Servers → + Add (Agent mode)

**SSE (remote HTTP)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "sse",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**STDIO (Docker)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

</details>

<details>
<summary><b>Amazon Q Developer (CLI & IDE)</b></summary>

**Q Developer CLI**

**Config file:** `~/.aws/amazonq/mcp.json` or workspace `.amazonq/mcp.json`

**HTTP server**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "http",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**STDIO (Docker)**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-e", "CODEALIVE_API_KEY=YOUR_API_KEY_HERE",
        "ghcr.io/codealive-ai/codealive-mcp:v0.3.0"
      ]
    }
  }
}
```

**Q Developer IDE (VS Code / JetBrains)**

**Global:** `~/.aws/amazonq/agents/default.json`
**Local (workspace):** `.aws/amazonq/agents/default.json`

**Minimal entry (HTTP):**
```json
{
  "mcpServers": {
    "codealive": {
      "type": "http",
      "url": "https://mcp.codealive.ai/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      },
      "timeout": 60000
    }
  }
}
```

Use the IDE UI: Q panel → Chat → tools icon → Add MCP Server → choose http or stdio.

</details>

<details>
<summary><b>JetBrains AI Assistant</b></summary>

> **Note:** JetBrains AI Assistant requires the `mcp-remote` workaround for connecting to remote HTTP MCP servers.

**Prerequisites:**
```bash
npm install -g mcp-remote
```

**Config file:** Settings/Preferences → AI Assistant → Model Context Protocol → Configure

Add this configuration:

```json
{
  "mcpServers": {
    "codealive": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.codealive.ai/api",
        "--header",
        "Authorization: Bearer ${CODEALIVE_API_KEY}"
      ],
      "env": {
        "CODEALIVE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
```

**For self-hosted deployments**, replace the URL:
```json
{
  "mcpServers": {
    "codealive": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://your-server:8000/api",
        "--header",
        "Authorization: Bearer ${CODEALIVE_API_KEY}"
      ],
      "env": {
        "CODEALIVE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}
```

See [JetBrains MCP Documentation](https://www.jetbrains.com/help/ai-assistant/mcp.html#workaround-for-remote-servers) for more details.

</details>

<details>
<summary><b>n8n</b></summary>

**Using AI Agent Node with MCP Tools**

1. Add an **AI Agent** node to your workflow
2. Configure the agent with MCP tools:
   ```
   Server URL: https://mcp.codealive.ai/api
   Authorization Header: Bearer YOUR_API_KEY_HERE
   ```

3. The server automatically handles n8n's extra parameters (sessionId, action, chatInput, toolCallId)
4. Use the three available tools:
   - `get_data_sources` - List available repositories
   - `codebase_search` - Search code semantically
   - `codebase_consultant` - Ask questions about code

**Example Workflow:**
```
Trigger → AI Agent (with CodeAlive MCP tools) → Process Response
```

**Note:** n8n middleware is built-in, so no special configuration is needed. The server will automatically strip n8n's extra parameters before processing tool calls.

</details>

---

## 🔧 Advanced: Local Development

**For developers who want to customize or contribute to the MCP server.**

### Prerequisites

*   Python 3.11+
*   [uv](https://github.com/astral-sh/uv) (recommended) or pip

### Installation

```bash
# Clone the repository
git clone https://github.com/CodeAlive-AI/codealive-mcp.git
cd codealive-mcp

# Setup with uv (recommended)
uv venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
uv pip install -e .

# Or setup with pip
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate  
pip install -e .
```

### Local Server Configuration

Once installed locally, configure your AI client to use the local server:

#### Claude Code (Local)
```bash
claude mcp add codealive-local /path/to/codealive-mcp/.venv/bin/python /path/to/codealive-mcp/src/codealive_mcp_server.py --env CODEALIVE_API_KEY=YOUR_API_KEY_HERE
```

#### Other Clients (Local)
Replace the Docker `command` and `args` with:
```json
{
  "command": "/path/to/codealive-mcp/.venv/bin/python",
  "args": ["/path/to/codealive-mcp/src/codealive_mcp_server.py"],
  "env": {
    "CODEALIVE_API_KEY": "YOUR_API_KEY_HERE"
  }
}
```

### Running HTTP Server Locally

```bash
# Start local HTTP server
export CODEALIVE_API_KEY="your_api_key_here"
python src/codealive_mcp_server.py --transport http --host localhost --port 8000

# Test health endpoint
curl http://localhost:8000/health
```

### Testing Your Local Installation

After making changes, quickly verify everything works:

```bash
# Quick smoke test (recommended)
make smoke-test

# Or run directly
python smoke_test.py

# With your API key for full testing
CODEALIVE_API_KEY=your_key python smoke_test.py

# Run unit tests
make unit-test

# Run all tests
make test
```

The smoke test verifies:
- Server starts and connects correctly
- All tools are registered
- Each tool responds appropriately
- Parameter validation works
- Runs in ~5 seconds

### Smithery Installation

Auto-install for Claude Desktop via [Smithery](https://smithery.ai/server/@CodeAlive-AI/codealive-mcp):

```bash
npx -y @smithery/cli install @CodeAlive-AI/codealive-mcp --client claude
```

---

## 🌐 Community Plugins

### Gemini CLI — CodeAlive Extension

**Repo:** https://github.com/akolotov/gemini-cli-codealive-extension

Gemini CLI extension that wires CodeAlive into your terminal with prebuilt slash commands and MCP config. It includes:
- `GEMINI.md` guidance so Gemini knows how to use CodeAlive tools effectively
- Slash commands: `/codealive:chat`, `/codealive:find`, `/codealive:search`
- Easy setup via Gemini CLI's extension system

**Install**
```bash
gemini extensions install https://github.com/akolotov/gemini-cli-codealive-extension
```

**Configure**
```bash
# Option 1: .env next to where you run `gemini`
CODEALIVE_API_KEY="your_codealive_api_key_here"

# Option 2: environment variable
export CODEALIVE_API_KEY="your_codealive_api_key_here"
gemini
```

---

## 🚢 HTTP Deployment (Self-Hosted & Cloud)

**Deploy the MCP server as an HTTP service for team-wide access or integration with self-hosted CodeAlive instances.**

### Deployment Options

The CodeAlive MCP server can be deployed as an HTTP service using Docker. This allows multiple AI clients to connect to a single shared instance, and enables integration with self-hosted CodeAlive deployments.

### Docker Compose (Recommended)

Create a `docker-compose.yml` file based on our example:

```bash
# Download the example
curl -O https://raw.githubusercontent.com/CodeAlive-AI/codealive-mcp/main/docker-compose.example.yml
mv docker-compose.example.yml docker-compose.yml

# Edit configuration (see below)
nano docker-compose.yml

# Start the service
docker compose up -d

# Check health
curl http://localhost:8000/health
```

**Configuration Options:**

1. **For CodeAlive Cloud (default):**
   - Remove `CODEALIVE_BASE_URL` environment variable (uses default `https://app.codealive.ai`)
   - Clients must provide their API key via `Authorization: Bearer YOUR_KEY` header

2. **For Self-Hosted CodeAlive:**
   - Set `CODEALIVE_BASE_URL` to your CodeAlive instance URL (e.g., `https://codealive.yourcompany.com`)
   - Clients must provide their API key via `Authorization: Bearer YOUR_KEY` header

See `docker-compose.example.yml` for the complete configuration template.

### Connecting AI Clients to Your Deployed Instance

Once deployed, configure your AI clients to use your HTTP endpoint:

**Claude Code:**
```bash
claude mcp add --transport http codealive http://your-server:8000/api --header "Authorization: Bearer YOUR_API_KEY_HERE"
```

**VS Code:**
```bash
code --add-mcp "{\"name\":\"codealive\",\"type\":\"http\",\"url\":\"http://your-server:8000/api\",\"headers\":{\"Authorization\":\"Bearer YOUR_API_KEY_HERE\"}}"
```

**Cursor / Other Clients:**
```json
{
  "mcpServers": {
    "codealive": {
      "url": "http://your-server:8000/api",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY_HERE"
      }
    }
  }
}
```

Replace `your-server:8000` with your actual deployment URL and port.

---

## 🐞 Troubleshooting

### Quick Diagnostics

1. **Test the hosted service:**
   ```bash
   curl https://mcp.codealive.ai/health
   ```

2. **Check your API key:**
   ```bash
   curl -H "Authorization: Bearer YOUR_API_KEY" https://app.codealive.ai/api/v1/data_sources
   ```

3. **Enable debug logging:** Add `--debug` to local server args

### Common Issues

- **"Connection refused"** → Check internet connection
- **"401 Unauthorized"** → Verify your API key  
- **"No repositories found"** → Check API key permissions in CodeAlive dashboard
- **Client-specific logs** → See your AI client's documentation for MCP logs

### Getting Help

- 📧 Email: support@codealive.ai
- 🐛 Issues: [GitHub Issues](https://github.com/CodeAlive-AI/codealive-mcp/issues)

---

## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

---

**Ready to supercharge your AI assistant with deep code understanding?**  
[Get started now →](https://app.codealive.ai/)