.do
Mcp

Sdk MCP

Model Context Protocol reference for sdk.do - Software Development Kit (Software Development Kit (SDK))

Sdk MCP

Software Development Kit (Software Development Kit (SDK))

Overview

The Model Context Protocol (MCP) provides AI models with direct access to sdk.do through a standardized interface.

Installation

pnpm add @modelcontextprotocol/sdk

Configuration

Add to your MCP server configuration:

{
  "mcpServers": {
    "sdk": {
      "command": "npx",
      "args": ["-y", "@dotdo/mcp-server"],
      "env": {
        "DO_API_KEY": "your-api-key"
      }
    }
  }
}

Tools

sdk/invoke

Main tool for sdk.do operations.

{
  "name": "sdk/invoke",
  "description": "Software Development Kit (Software Development Kit (SDK))",
  "inputSchema": {
    "type": "object",
    "properties": {
      "operation": {
        "type": "string",
        "description": "Operation to perform"
      },
      "parameters": {
        "type": "object",
        "description": "Operation parameters"
      }
    },
    "required": ["operation"]
  }
}

Usage in AI Models

Claude Desktop

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "sdk": {
      "command": "npx",
      "args": ["-y", "@dotdo/mcp-server", "--tool=sdk"],
      "env": {
        "DO_API_KEY": "undefined"
      }
    }
  }
}

OpenAI GPTs

# Custom GPT configuration
tools:
  - type: mcp
    server: sdk
    operations:
      - invoke
      - query
      - execute

Custom Integration

import { Client } from '@modelcontextprotocol/sdk/client/index.js'
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'

const transport = new StdioClientTransport({
  command: 'npx',
  args: ['-y', '@dotdo/mcp-server', '--tool=sdk'],
})

const client = new Client(
  {
    name: 'sdk-client',
    version: '1.0.0',
  },
  {
    capabilities: {},
  }
)

await client.connect(transport)

// Call tool
const result = await client.callTool({
  name: 'sdk/invoke',
  arguments: {
    operation: 'sdk',
    parameters: {},
  },
})

Tool Definitions

Available Tools

{
  "tools": [
    {
      "name": "sdk/invoke",
      "description": "Invoke sdk.do",
      "inputSchema": {
        /* ... */
      }
    },
    {
      "name": "sdk/query",
      "description": "Query sdk.do resources",
      "inputSchema": {
        /* ... */
      }
    },
    {
      "name": "sdk/status",
      "description": "Check sdk.do status",
      "inputSchema": {
        /* ... */
      }
    }
  ]
}

Resources

Available Resources

{
  "resources": [
    {
      "uri": "sdk://config",
      "name": "Sdk Configuration",
      "mimeType": "application/json"
    },
    {
      "uri": "sdk://docs",
      "name": "Sdk Documentation",
      "mimeType": "text/markdown"
    }
  ]
}

Prompts

Pre-configured Prompts

{
  "prompts": [
    {
      "name": "sdk-quick-start",
      "description": "Quick start guide for sdk.do",
      "arguments": []
    },
    {
      "name": "sdk-best-practices",
      "description": "Best practices for sdk.do",
      "arguments": []
    }
  ]
}

Examples

Basic Usage

// AI model calls tool via MCP
mcp call sdk/run

With Parameters

// Call with parameters
await mcp.callTool('sdk/invoke', {
  operation: 'process',
  parameters: {
    // Operation-specific parameters
  },
  options: {
    timeout: 30000,
  },
})

Error Handling

try {
  const result = await mcp.callTool('sdk/invoke', {
    operation: 'process',
  })
  return result
} catch (error) {
  if (error.code === 'TOOL_NOT_FOUND') {
    console.error('Sdk tool not available')
  } else {
    throw error
  }
}

AI Integration Patterns

Agentic Workflows

// AI agent uses sdk.do in workflow
const workflow = {
  steps: [
    {
      tool: 'sdk/invoke',
      operation: 'analyze',
      input: 'user-data',
    },
    {
      tool: 'sdk/process',
      operation: 'transform',
      input: 'analysis-result',
    },
  ],
}

Chain of Thought

AI models can reason about sdk.do operations:

User: "I need to process this data"

AI: "I'll use the sdk tool to:
1. Validate the data format
2. Process it through sdk.do
3. Return the results

Let me start..."

[Calls: mcp call sdk/run]

Server Implementation

Custom MCP Server

import { Server } from '@modelcontextprotocol/sdk/server/index.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'

const server = new Server(
  {
    name: 'sdk-server',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
      resources: {},
      prompts: {},
    },
  }
)

// Register tool
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'sdk/invoke') {
    // Handle sdk.do operation
    return {
      content: [
        {
          type: 'text',
          text: JSON.stringify(result),
        },
      ],
    }
  }
})

const transport = new StdioServerTransport()
await server.connect(transport)

Best Practices

  1. Tool Design - Keep tools focused and single-purpose
  2. Error Messages - Provide clear, actionable errors
  3. Documentation - Include examples in tool descriptions
  4. Rate Limiting - Implement appropriate limits
  5. Security - Validate all inputs from AI models
  6. Monitoring - Track tool usage and errors