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/sdkConfiguration
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
- executeCustom 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/runWith 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
- Tool Design - Keep tools focused and single-purpose
- Error Messages - Provide clear, actionable errors
- Documentation - Include examples in tool descriptions
- Rate Limiting - Implement appropriate limits
- Security - Validate all inputs from AI models
- Monitoring - Track tool usage and errors