.do
Mcp

Apis MCP

Model Context Protocol reference for apis.do - Unified Application Programming Interface (API) gateway for all services

Apis MCP

Unified Application Programming Interface (API) gateway for all services

Overview

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

Installation

pnpm add @modelcontextprotocol/sdk

Configuration

Add to your MCP server configuration:

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

Tools

apis/invoke

Main tool for apis.do operations.

{
  "name": "apis/invoke",
  "description": "Unified Application Programming Interface (API) gateway for all services",
  "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": {
    "apis": {
      "command": "npx",
      "args": ["-y", "@dotdo/mcp-server", "--tool=apis"],
      "env": {
        "DO_API_KEY": "undefined"
      }
    }
  }
}

OpenAI GPTs

# Custom GPT configuration
tools:
  - type: mcp
    server: apis
    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=apis'],
})

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

await client.connect(transport)

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

Tool Definitions

Available Tools

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

Resources

Available Resources

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

Prompts

Pre-configured Prompts

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

Examples

Basic Usage

// AI model calls tool via MCP
mcp call apis/call

With Parameters

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

Error Handling

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

AI Integration Patterns

Agentic Workflows

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

Chain of Thought

AI models can reason about apis.do operations:

User: "I need to process this data"

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

Let me start..."

[Calls: mcp call apis/call]

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: 'apis-server',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
      resources: {},
      prompts: {},
    },
  }
)

// Register tool
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'apis/invoke') {
    // Handle apis.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