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/sdkConfiguration
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
- 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=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/callWith 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
- 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