Advanced Graph RAG MCP Server with sophisticated graph structures, operators, and agentic capabilities for AI agents
npm install @zrald/graph-rag-mcp-server

A comprehensive Model Context Protocol (MCP) server implementing advanced Graph RAG (Retrieval-Augmented Generation) architecture with sophisticated graph structures, operators, and agentic capabilities for AI agents.
#### Node Operators
- VDB Operator: Vector similarity search for semantic relevance
- PPR Operator: Personalized PageRank for authority analysis
#### Relationship Operators
- OneHop Operator: Direct neighborhood exploration
- Aggregator Operator: Multi-relationship synthesis
#### Chunk Operators
- FromRel Operator: Trace relationships back to source chunks
- Occurrence Operator: Entity co-occurrence analysis
#### Subgraph Operators
- KHopPath Operator: Multi-step path finding between entities
- Steiner Operator: Minimal connecting networks construction
``bashInstall via npm
npm install @zrald/graph-rag-mcp-server
$3
`bash
Clone the repository for development
git clone https://github.com/augment-code/graph-rag-mcp-server.git
cd graph-rag-mcp-serverInstall dependencies
npm installSet up environment variables
cp .env.example .env
Edit .env with your configuration
Build the project
npm run buildStart the server
npm start
`โก Quick Start
`javascript
import { GraphRAGMCPServer } from '@zrald/graph-rag-mcp-server';// Initialize the server
const server = new GraphRAGMCPServer();
// Start the MCP server
await server.initialize();
await server.start();
console.log('Graph RAG MCP Server is running!');
`$3
`bash
Start the server directly
graph-rag-mcp-serverOr with custom configuration
NEO4J_URI=bolt://localhost:7687 graph-rag-mcp-server
`๐ง Configuration
$3
- Neo4j Database: For graph storage and querying
- Node.js 18+: Runtime environment
- Memory: Minimum 4GB RAM recommended$3
See .env.example for all configuration options.Key configurations:
-
NEO4J_URI: Neo4j database connection string
- NEO4J_USERNAME/PASSWORD: Database credentials
- VECTOR_DIMENSION: Embedding dimension (default: 384)
- MAX_VECTOR_ELEMENTS: Vector store capacity๐ ๏ธ Usage
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`typescript
// Create an intelligent query plan
const plan = await server.createQueryPlan(
"Find relationships between artificial intelligence and machine learning",
{ reasoning_type: "analytical" }
);// Execute the plan
const result = await server.executeQueryPlan(plan.id);
`$3
`typescript
// Vector similarity search
const vdbResult = await server.vdbSearch({
query_embedding: [0.1, 0.2, ...], // 384-dimensional vector
top_k: 10,
similarity_threshold: 0.7,
node_types: ["entity", "concept"]
});// Personalized PageRank analysis
const pprResult = await server.pageRankAnalysis({
seed_nodes: ["ai_node_1", "ml_node_2"],
damping_factor: 0.85,
max_iterations: 100
});
// Multi-hop path finding
const pathResult = await server.pathFinding({
source_nodes: ["source_entity"],
target_nodes: ["target_entity"],
max_hops: 3,
path_limit: 10
});
`$3
`typescript
// Add nodes to the knowledge graph
await server.addNodes({
nodes: [
{
id: "ai_concept",
type: "concept",
label: "Artificial Intelligence",
properties: { domain: "technology" },
embedding: [0.1, 0.2, ...] // Optional
}
]
});// Add relationships
await server.addRelationships({
relationships: [
{
id: "rel_1",
source_id: "ai_concept",
target_id: "ml_concept",
type: "INCLUDES",
weight: 0.9,
confidence: 0.95
}
]
});
`$3
`typescript
// Adaptive reasoning
const reasoningResult = await server.adaptiveReasoning({
reasoning_query: "How does machine learning enable artificial intelligence?",
reasoning_type: "causal",
max_iterations: 5,
confidence_threshold: 0.8
});// Multi-modal fusion
const fusionResult = await server.multiModalFusion({
fusion_query: "Compare AI approaches across different domains",
graph_types: ["knowledge", "passage", "trees"],
fusion_strategy: "weighted_average"
});
`๐๏ธ Architecture
$3
`
src/
โโโ core/ # Core infrastructure
โ โโโ graph-database.ts # Neo4j integration
โ โโโ vector-store.ts # Vector embeddings store
โโโ operators/ # Graph RAG operators
โ โโโ base-operator.ts # Base operator class
โ โโโ node-operators.ts # VDB, PPR operators
โ โโโ relationship-operators.ts # OneHop, Aggregator
โ โโโ chunk-operators.ts # FromRel, Occurrence
โ โโโ subgraph-operators.ts # KHopPath, Steiner
โโโ execution/ # Execution engine
โ โโโ operator-executor.ts # Orchestration logic
โโโ planning/ # Query planning
โ โโโ query-planner.ts # Intelligent planning
โโโ utils/ # Utilities
โ โโโ embedding-generator.ts # Text embeddings
โ โโโ graph-builders.ts # Graph construction
โโโ types/ # Type definitions
โ โโโ graph.ts # Core types
โโโ mcp-server.ts # MCP server implementation
โโโ index.ts # Entry point
`$3
1. Query Input โ Query Planner analyzes intent and complexity
2. Plan Creation โ Intelligent operator chain generation
3. Execution โ Operator orchestration with chosen pattern
4. Result Fusion โ Combine results using fusion strategy
5. Response โ Structured output with metadata๐ MCP Tools Reference
$3
- create_query_plan: Generate intelligent execution plans
- execute_query_plan: Execute pre-created plans
- execute_operator_chain: Run custom operator chains$3
- vdb_search: Vector similarity search
- pagerank_analysis: Authority analysis
- neighborhood_exploration: Direct relationship exploration
- relationship_aggregation: Multi-relationship synthesis
- chunk_tracing: Source chunk identification
- co_occurrence_analysis: Entity co-occurrence patterns
- path_finding: Multi-hop path discovery
- steiner_tree: Minimal connecting networks$3
- create_knowledge_graph: Build new graph structures
- add_nodes: Insert nodes into graphs
- add_relationships: Create relationships
- add_chunks: Add text chunks to vector store$3
- graph_analytics: Comprehensive graph statistics
- operator_performance: Performance metrics
- adaptive_reasoning: Complex reasoning capabilities
- multi_modal_fusion: Cross-graph analysis๐ MCP Resources
-
graph://knowledge-graph: Access to graph structure
- graph://vector-store: Vector embeddings information
- graph://operator-registry: Available operators
- graph://execution-history: Performance history๐งช Testing
`bash
Run tests
npm testRun with coverage
npm run test:coverageLint code
npm run lintFormat code
npm run format
`๐ Performance
$3
- Vector Search: Sub-100ms for 10K embeddings
- PageRank: Converges in <50 iterations for most graphs
- Path Finding: Handles graphs with 100K+ nodes
- Parallel Execution: 3-5x speedup over sequential$3
- Intelligent Caching: Query plan and result caching
- Batch Processing: Efficient bulk operations
- Adaptive Thresholds: Dynamic parameter adjustment
- Resource Management: Memory and CPU optimization๐ค Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request
๐ License
MIT License - see LICENSE file for details.
๐ Support
- Documentation: See
/docs` directory- [ ] Real-time graph updates
- [ ] Distributed execution
- [ ] Advanced ML integration
- [ ] Custom operator development SDK
- [ ] Graph visualization tools
- [ ] Performance dashboard
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Built with โค๏ธ for the AI agent ecosystem. Empowering intelligent systems with sophisticated graph-based reasoning capabilities.