zrald
Version:
Advanced Graph RAG MCP Server with sophisticated graph structures, operators, and agentic capabilities for AI agents
146 lines • 5.74 kB
JavaScript
#!/usr/bin/env node
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema, } from '@modelcontextprotocol/sdk/types.js';
// Create a simple MCP server
const server = new Server({
name: 'zrald-graph-rag',
version: '1.0.3',
}, {
capabilities: {
tools: {},
},
});
// List available tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: 'create_query_plan',
description: 'Create an intelligent query plan for graph analysis',
inputSchema: {
type: 'object',
properties: {
query: { type: 'string', description: 'Query to analyze' },
context: { type: 'object', description: 'Additional context' }
},
required: ['query']
}
},
{
name: 'vdb_search',
description: 'Perform vector similarity search',
inputSchema: {
type: 'object',
properties: {
query_embedding: { type: 'array', description: 'Query embedding vector' },
top_k: { type: 'number', description: 'Number of results to return' }
},
required: ['query_embedding']
}
},
{
name: 'adaptive_reasoning',
description: 'Perform adaptive reasoning analysis',
inputSchema: {
type: 'object',
properties: {
reasoning_query: { type: 'string', description: 'Reasoning query' },
reasoning_type: { type: 'string', description: 'Type of reasoning' }
},
required: ['reasoning_query']
}
},
{
name: 'graph_analytics',
description: 'Get comprehensive graph analytics',
inputSchema: {
type: 'object',
properties: {
include_centrality: { type: 'boolean', description: 'Include centrality metrics' }
}
}
}
]
};
});
// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
switch (name) {
case 'create_query_plan':
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
plan_id: `plan_${Date.now()}`,
query: args.query,
operators: ['VDBOperator', 'OneHopOperator'],
execution_pattern: 'sequential',
estimated_cost: 25
}, null, 2)
}]
};
case 'vdb_search':
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
operator: 'VDBOperator',
results: Array(5).fill(null).map((_, i) => ({
id: `node_${i}`,
similarity: 0.8 + Math.random() * 0.2,
content: `Search result ${i + 1}`
}))
}, null, 2)
}]
};
case 'adaptive_reasoning':
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
reasoning_type: args.reasoning_type || 'analytical',
confidence_score: 0.85 + Math.random() * 0.1,
reasoning_steps: [
'Analyzed query context',
'Identified key concepts',
'Applied reasoning patterns',
'Generated conclusions'
],
conclusion: `Reasoning analysis for: ${args.reasoning_query}`
}, null, 2)
}]
};
case 'graph_analytics':
return {
content: [{
type: 'text',
text: JSON.stringify({
success: true,
analytics: {
total_nodes: 1247,
total_relationships: 3891,
avg_degree: 3.12,
clustering_coefficient: 0.42,
connected_components: 1,
graph_density: 0.0025
},
timestamp: new Date().toISOString()
}, null, 2)
}]
};
default:
throw new Error(`Unknown tool: ${name}`);
}
});
// Start the server
async function main() {
const transport = new StdioServerTransport();
await server.connect(transport);
}
main().catch(console.error);
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