https://github.com/rocket-connect/mcp-rag
A lightweight wrapper around AI SDK that intelligently indexes and retrieves MCP tools using graph-based vector search.
https://github.com/rocket-connect/mcp-rag
ai augmented characters embeddings generative-ai limits mcp neo4j performance rag scale tools vector-search
Last synced: 5 months ago
JSON representation
A lightweight wrapper around AI SDK that intelligently indexes and retrieves MCP tools using graph-based vector search.
- Host: GitHub
- URL: https://github.com/rocket-connect/mcp-rag
- Owner: rocket-connect
- License: mit
- Created: 2025-11-13T05:25:12.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-11-17T15:28:07.000Z (5 months ago)
- Last Synced: 2025-11-17T17:20:19.530Z (5 months ago)
- Topics: ai, augmented, characters, embeddings, generative-ai, limits, mcp, neo4j, performance, rag, scale, tools, vector-search
- Language: TypeScript
- Homepage:
- Size: 1.12 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# MCP RAG
A lightweight wrapper around AI SDK that intelligently indexes and retrieves MCP tools using graph-based vector search.
## What It Does
MCP RAG indexes your MCP toolset into a graph structure and uses Neo4j-powered vector search to retrieve relevant tool subsets from large collections. This dramatically reduces context overhead when working with extensive tool libraries.
See our [benchmarks](./benchmarks/latest.md) for performance improvements.
## Benchmarks
MCP RAG sees improvements in both efficiency and performance compared to baseline tool selection, while maintaining the same level of accuracy.
**Benchmark Methodology:** Tests simulate a realistic conversation with 5 sequential prompts, each triggering a different tool as context accumulates—mirroring real-world multi-turn interactions. All tests use the complete toolset from the [GitHub MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/github) (90+ tools) to represent authentic large-scale tool selection scenarios.
See the proof in the pudding:
**[Base Tool Selection Results](./benchmarks/results/base-tool-selection/latest.md)** - Baseline approach passing all tools to the model.
**[RAG Tool Selection Results](./benchmarks/results/rag-tool-selection/latest.md)** - RAG-powered intelligent filtering with vector search.
**[View Test Suite](./benchmarks/src/rag.test.ts)** - Complete benchmark implementation and test cases.
## Installation
[](https://badge.fury.io/js/@mcp-rag%2Fclient)
```bash
npm install @mcp-rag/client @ai-sdk/openai neo4j-driver ai
```
Set your OpenAI API key:
```bash
export OPENAI_API_KEY=your_key_here
```
## Quick Start
```typescript
import { createMCPRag } from '@mcp-rag/client'
import { openai } from '@ai-sdk/openai'
import neo4j from 'neo4j-driver'
import { tool } from 'ai'
import { z } from 'zod'
const driver = neo4j.driver(
'neo4j://localhost:7687',
neo4j.auth.basic('neo4j', 'password')
)
const rag = createMCPRag({
model: openai('gpt-4'),
neo4j: driver,
tools: {
searchDocs: tool({
/* ... */
}),
queryDatabase: tool({
/* ... */
}),
sendEmail: tool({
/* ... */
}),
fetchWeather: tool({
/* ... */
}),
analyzeImage: tool({
/* ... */
}),
// ... hundreds more tools
},
})
await rag.sync()
const result = await rag.generateText({
prompt: 'Search for API docs',
})
```
What does rag.sync() do?
The `sync()` method performs a complete synchronization of your tools to Neo4j, creating the graph structure needed for semantic search. Here's what happens under the hood:
1. **Creates Vector Index**: Sets up a Neo4j vector index for similarity search using 1536-dimensional embeddings (OpenAI's `text-embedding-3-small` model)
2. **Generates Embeddings**: For each tool in your toolset, it creates embeddings for:
- The tool itself (name + description)
- Each parameter (name + description)
- The return type
3. **Builds Graph Structure**: Creates a graph in Neo4j with the following relationships:
- `ToolSet` nodes that group tools together
- `Tool` nodes with their embeddings
- `Parameter` nodes connected to tools via `HAS_PARAM` relationships
- `ReturnType` nodes connected to tools via `RETURNS` relationships
4. **Idempotent by Design**: The sync process uses `MERGE` operations, so running it multiple times won't create duplicates. It will update existing nodes if the toolset has changed.
**When to call it:**
- After initial client creation (required before first use)
- After adding or removing tools with `addTool()` or `removeTool()`
- To force a re-index of your tools
The sync process is optimized to only run when necessary - subsequent calls to `generateText()` won't re-sync unless you explicitly call `sync()` again or modify the toolset.
What does rag.generateText() do?
The `generateText()` method is a smart wrapper around the AI SDK's `generateText` function that adds automatic tool selection. Here's the workflow:
1. **Ensures Migration**: Automatically calls the sync process if tools haven't been indexed yet
2. **Semantic Tool Selection**:
- Generates an embedding for your prompt
- Performs a Neo4j vector similarity search to find the most relevant tools
- By default, selects up to 10 tools (configurable via `maxActiveTools`)
- You can override this by passing `activeTools` array explicitly
3. **Calls AI SDK**: Passes only the selected subset of tools to the AI SDK's native `generateText` function along with your prompt and any additional options
4. **Returns Full Result**: Returns the complete AI SDK result wrapped in a `GenerateTextResultWrapper` object, giving you access to:
- Tool calls made by the model
- Token usage statistics
- Response content
- All other AI SDK metadata
**Key Benefits:**
- **Reduced Context Size**: Only relevant tools are sent to the LLM, saving tokens
- **Better Performance**: Fewer tools mean faster response times
- **Same AI SDK Experience**: Accepts all standard AI SDK parameters and returns familiar result structures
## Features
- **Graph-based indexing** – Tools are indexed with their relationships and metadata
- **Vector search** – Neo4j-powered semantic search for tool retrieval
- **AI SDK compatible** – Drop-in wrapper that works with your existing AI SDK setup
- **Selective loading** – Only load the tools you need for each request
## License
MIT [rconnect.tech](https://www.rconnect.tech)