@mmlotfy/intellicodemcp
Version:
IntelliCodeMCP - Advanced AI Model Context Protocol System for intelligent code management and orchestration
262 lines • 11.6 kB
JavaScript
#!/usr/bin/env node
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const index_js_1 = require("@modelcontextprotocol/sdk/server/index.js");
const stdio_js_1 = require("@modelcontextprotocol/sdk/server/stdio.js");
const types_js_1 = require("@modelcontextprotocol/sdk/types.js");
const memory_bank_1 = require("../tools/memory-bank");
const code_trace_1 = require("../tools/code-trace");
const smartcontext_weaver_1 = require("../tools/smartcontext-weaver");
const smartthink_weaver_1 = require("../tools/smartthink-weaver");
const performance_orchestrator_1 = require("../tools/performance-orchestrator");
const web_search_1 = require("../tools/web-search");
async function startMCPServer() {
const server = new index_js_1.Server({
name: 'intellicode-mcp',
version: '2.3.0',
}, {
capabilities: {
tools: {},
},
});
// List available tools
server.setRequestHandler(types_js_1.ListToolsRequestSchema, async () => {
return {
tools: [
{
name: 'memory_bank',
description: 'File-based storage system for project data with search capabilities',
inputSchema: {
type: 'object',
properties: {
action: {
type: 'string',
enum: ['read', 'write', 'append', 'search'],
description: 'Operation to perform: read, write, append, or search files'
},
category: {
type: 'string',
enum: ['docs', 'threads', 'knots', 'technical', 'profiles', 'search'],
description: 'Storage category for organizing files'
},
file_name: {
type: 'string',
description: 'Name of the file to operate on'
},
content: {
type: 'string',
description: 'Content for write/append operations'
},
query: {
type: 'string',
description: 'Search query for finding files'
}
},
required: ['action', 'category', 'file_name']
}
},
{
name: 'code_trace',
description: 'Real-time TypeScript and ESLint error tracking',
inputSchema: {
type: 'object',
properties: {
project_path: {
type: 'string',
description: 'Path to the project directory to analyze'
},
check_type: {
type: 'string',
enum: ['typescript', 'eslint', 'all'],
description: 'Type of code analysis: typescript, eslint, or all'
},
fix: {
type: 'boolean',
description: 'Whether to automatically fix ESLint errors'
}
},
required: ['project_path', 'check_type']
}
},
{
name: 'smartcontext_weaver',
description: 'Long conversation context management and summarization',
inputSchema: {
type: 'object',
properties: {
thread_id: {
type: 'string',
description: 'Unique identifier for the conversation thread'
},
task_id: {
type: 'string',
description: 'Unique identifier for the specific task'
},
max_tokens: {
type: 'number',
description: 'Maximum number of tokens for the summary'
},
query: {
type: 'string',
description: 'Search query to enhance context'
}
}
}
},
{
name: 'smartthink_weaver',
description: 'Multi-agent reasoning for complex problem solving',
inputSchema: {
type: 'object',
properties: {
problem: {
type: 'string',
description: 'The complex problem statement to analyze and solve'
},
context: {
type: 'string',
description: 'Additional context or background information'
},
max_steps: {
type: 'number',
description: 'Maximum number of reasoning steps to perform'
},
query: {
type: 'string',
description: 'Search query for additional context from memory'
}
},
required: ['problem']
}
},
{
name: 'performance_orchestrator',
description: 'Intelligent AI model selection and task orchestration',
inputSchema: {
type: 'object',
properties: {
task: {
type: 'object',
properties: {
id: {
type: 'string',
description: 'Unique identifier for the task'
},
input: {
type: 'string',
description: 'The task description or input to process'
},
priority: {
type: 'string',
enum: ['low', 'medium', 'high'],
description: 'Task priority level'
},
type: {
type: 'string',
enum: ['simple', 'code_writing', 'analytical', 'complex'],
description: 'Type of task for optimal model selection'
},
context: {
type: 'string',
description: 'Additional context for the task'
}
},
required: ['id', 'input', 'priority']
}
},
required: ['task']
}
},
{
name: 'web_search',
description: 'Search the web using SerpAPI for real-time information',
inputSchema: {
type: 'object',
properties: {
query: {
type: 'string',
description: 'Search query to find information on the web'
},
num_results: {
type: 'number',
minimum: 1,
maximum: 20,
description: 'Number of search results to return (1-20)'
},
api_key: {
type: 'string',
description: 'SerpAPI key (optional if set in environment variable)'
}
},
required: ['query']
}
}
]
};
});
// Handle tool calls
server.setRequestHandler(types_js_1.CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
try {
let result;
switch (name) {
case 'memory_bank':
result = await (0, memory_bank_1.executeMemoryFile)(args);
break;
case 'code_trace':
result = await (0, code_trace_1.executeCodeTrace)(args);
break;
case 'smartcontext_weaver':
result = await (0, smartcontext_weaver_1.executeSmartContext)(args);
break;
case 'smartthink_weaver':
result = await (0, smartthink_weaver_1.executeSmartThink)(args);
break;
case 'performance_orchestrator':
result = await (0, performance_orchestrator_1.executePerformanceOrchestrator)(args);
break;
case 'web_search':
result = await (0, web_search_1.executeWebSearch)(args);
break;
default:
throw new Error(`Unknown tool: ${name}`);
}
return {
content: [
{
type: 'text',
text: JSON.stringify(result, null, 2)
}
]
};
}
catch (error) {
return {
content: [
{
type: 'text',
text: `Error: ${error.message}`
}
],
isError: true
};
}
});
// Error handling
server.onerror = (error) => {
console.error('[MCP Error]', error);
};
process.on('SIGINT', async () => {
await server.close();
process.exit(0);
});
// Start server
const transport = new stdio_js_1.StdioServerTransport();
await server.connect(transport);
console.error('IntelliCodeMCP server running on stdio');
}
startMCPServer().catch((error) => {
console.error('Failed to start server:', error);
process.exit(1);
});
//# sourceMappingURL=intellicodemcp.js.map