What is Do Kb Mcp

do-kb-mcp is a Model Context Protocol (MCP) server designed for Digital Ocean Agents and their knowledge bases, enabling AI tools like Claude to search and retrieve information efficiently.

Use cases

Use cases for do-kb-mcp include enhancing search capabilities in AI applications, managing knowledge bases for Digital Ocean Agents, and optimizing information retrieval processes in cloud environments.

How to use

To use do-kb-mcp, install the necessary dependencies with ‘npm install’, set up your environment variables, and deploy the server either locally for development or securely for production following the provided deployment guide.

Key features

Key features include four search methods (Basic, Rewrite, Step-back, Sub-queries), multi-agent support, chunk-focused retrieval for cost optimization, scalable Cloudflare Workers deployment, dual-layer rate limiting, and enhanced security measures.

Where to use

do-kb-mcp can be used in various fields such as cloud computing, AI development, and knowledge management systems, particularly where efficient information retrieval from multiple agents is required.

Digital Ocean Agent MCP Server

A Model Context Protocol (MCP) server that provides search capabilities for Digital Ocean Agent knowledge bases. This server enables Claude and other MCP-compatible AI tools to search and retrieve information from Digital Ocean Agent instances.

Features

• 4 Search Methods: Basic, Rewrite (default), Step-back, and Sub-queries retrieval strategies
• Multi-Agent Support: Configure single or multiple DO Agent instances
• Chunk-Focused: Returns document chunks with minimal AI overhead for cost optimization
• Cloudflare Workers Deployment: Scalable serverless deployment
• Dual-Layer Rate Limiting: Per-IP and global rate limits for cost protection
• Security Hardened: Sanitized error messages, request timeouts, secure agent selection

Search Tools

1. do_agent_basic_search - Basic search without query enhancement (retrieval_method: "none")
2. do_agent_rewrite_search - Search with AI query rewriting (retrieval_method: "rewrite")
3. do_agent_step_back_search - Step-back strategy for broader context (retrieval_method: "step_back")
4. do_agent_sub_queries_search - Sub-queries method for comprehensive retrieval (retrieval_method: "sub_queries")

Additional management tools:

• list_do_agents - List all configured agent instances
• get_current_do_agent - Get default agent information

Quick Start

1. Install Dependencies

npm install

2. Secure Deployment Setup

⚠️ IMPORTANT: Follow the complete deployment guide for secure production setup.

Quick local development:

# Copy environment template (local dev only)
cp .env.example .env
# Edit .env with your endpoint and token
npm run dev

Production deployment:

# 1. Edit wrangler.toml with your endpoint (public)
# 2. Set token as secret (never committed):
wrangler secret put DO_AGENT_TOKEN
# 3. Deploy securely:
npm run deploy

📖 Read the full deployment guide for step-by-step instructions, security best practices, and troubleshooting.

Usage Examples

Basic Search

{
  "tool": "do_agent_basic_search",
  "parameters": {
    "query": "How do I configure load balancers?",
    "k": 5
  }
}

Rewrite Search (Recommended)

{
  "tool": "do_agent_rewrite_search",
  "parameters": {
    "query": "database backup procedures",
    "k": 10,
    "include_ai_response": false
  }
}

Multi-Agent Search

{
  "tool": "do_agent_step_back_search",
  "parameters": {
    "query": "troubleshooting connection issues",
    "agent_name": "agent_2"
  }
}

Parameters

Configuration

Environment Variables

• DO_AGENT_ENDPOINT - Single agent endpoint URL
• DO_AGENT_TOKEN - Single agent bearer token
• DO_AGENT_ENDPOINTS - Comma-separated agent endpoints
• DO_AGENT_TOKENS - Comma-separated bearer tokens
• DO_AGENT_DESCRIPTIONS - Comma-separated agent descriptions

Rate Limiting

Per-IP Rate Limiting:

• RATE_LIMIT_PER_WINDOW - Max requests per IP per time window (default: 75)
• RATE_LIMIT_WINDOW_SECONDS - Time window in seconds (default: 120)

Global Rate Limiting:

• GLOBAL_RATE_LIMIT_PER_WINDOW - Max requests globally per time window (default: 100)
• GLOBAL_RATE_LIMIT_WINDOW_SECONDS - Global time window in seconds (default: 120)

The server enforces both per-IP and global rate limits to prevent cost explosion. Rate limit errors return specific messages indicating whether the per-IP or global limit was exceeded.

Security Features

• Request Timeouts: 30-second timeout on all DO Agent API calls
• Error Sanitization: Backend errors are sanitized to prevent information leakage
• Secure Agent Selection: Uses agent names (agent_1, agent_2) instead of exposing internal endpoints
• Input Validation: Comprehensive parameter validation with specific error messages

Response Filtering

By default, AI responses are filtered out to focus on retrieval chunks and minimize costs. Set include_ai_response: true to include the full AI response.

Fixed Settings

The following are automatically configured for optimal performance:

• stream: false - Non-streaming responses
• include_retrieval_info: true - Always include document chunks
• max_tokens: 50 - Minimal tokens for cost optimization

Architecture

This MCP server acts as a bridge between MCP clients (like Claude) and Digital Ocean Agents:

MCP Client (Claude) ↔ MCP Server ↔ DO Agent API ↔ Knowledge Base

The server optimizes for:

• Cost: Minimal token usage, cheapest DO models
• Relevance: Advanced retrieval methods for better chunks
• Scalability: Cloudflare Workers deployment
• Flexibility: Single/multi-agent configurations

Development

Built with:

• Model Context Protocol
• Cloudflare Workers
• Zod for schema validation
• TypeScript for type safety

License

MIT