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: 2 months ago
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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 (8 months ago)
- Default Branch: main
- Last Pushed: 2025-11-17T15:28:07.000Z (8 months ago)
- Last Synced: 2025-11-17T17:20:19.530Z (8 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
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README
# MCP RAG
[](https://badge.fury.io/js/@mcp-rag%2Fclient) [](https://github.com/rocket-connect/mcp-rag/actions/workflows/ci.yml) [](https://opensource.org/licenses/MIT)
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.
## 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 (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-4o-mini'),
openaiApiKey: process.env.OPENAI_API_KEY || '',
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
## Examples
### GitHub MCP Server Demo
Want to see MCP RAG in action? Check out our complete example that demonstrates intelligent tool selection with the GitHub MCP Server's 93 tools:
**[📖 View GitHub Example →](./examples/github/README.md)**
This example shows:
- How to mock and index all 93 GitHub MCP server tools
- Vector similarity search selecting the top 10 most relevant tools
- Real-world tool selection with detailed debug output
- Interactive testing with different prompts
Perfect for understanding how MCP RAG reduces context overhead in large toolsets!
## 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
## API Reference
### `createMCPRag(config)`
Creates an MCP RAG client.
```typescript
const rag = createMCPRag({
model: LanguageModel, // AI SDK model (required)
neo4j: Driver, // Neo4j driver (required)
tools: Record, // AI SDK tools (required)
openaiApiKey: string, // For embeddings (required)
maxActiveTools?: number, // Default: 10
hashFunction?: (input: string) => string, // Custom hash function
dangerouslyAllowBrowser?: boolean, // Enable browser usage
migration?: {
shouldMigrate?: (session) => Promise,
migrate?: (session, tools) => Promise,
onBeforeMigrate?: (statements) => Promise,
}
})
```
### Client Methods
| Method | Description |
| --------------------------- | ------------------------------------------------------- |
| `sync(options?)` | Sync tools to Neo4j. Returns `{ hash: string }` |
| `generateText(options)` | Generate text with semantic tool selection |
| `getActiveTools(options)` | Get semantically selected tools without generating text |
| `addTool(name, tool)` | Add a tool (updates hash, requires re-sync) |
| `removeTool(name)` | Remove a tool (updates hash, requires re-sync) |
| `getTools()` | Get all registered tools |
| `getToolsetHash()` | Get current toolset hash |
| `getToolsetByHash(hash)` | Retrieve toolset info from Neo4j |
| `deleteToolsetByHash(hash)` | Delete a toolset from Neo4j |
### `getActiveTools(options)`
Get semantically selected tools to use with your own AI SDK calls:
```typescript
const { tools, names } = await rag.getActiveTools({
prompt: 'What is the weather?',
maxTools: 5, // optional, defaults to maxActiveTools
})
// Use with AI SDK directly
const result = await generateText({
model: openai('gpt-4o'),
tools,
prompt: 'What is the weather?',
})
```
## Toolset Hashes
Hashes uniquely identify toolset versions for change detection and multi-version support.
### How Hashes Work
1. **Serialization** – Tools are deep-cloned (excluding `execute` functions)
2. **Sorting** – Tools sorted by name, all nested keys sorted recursively
3. **Hashing** – JSON string passed to hash function
```typescript
// Hash changes when tools change
const hash1 = rag.getToolsetHash()
rag.addTool('newTool', myTool)
const hash2 = rag.getToolsetHash() // Different from hash1
```
### Default Hash
Uses a bitwise hash returning `toolset-`:
```typescript
const hash = rag.getToolsetHash() // "toolset-a1b2c3d4"
```
### Custom Hash Function
For browser environments or custom requirements:
```typescript
const rag = createMCPRag({
// ...
hashFunction: input => {
// Web Crypto API example
const encoder = new TextEncoder()
const data = encoder.encode(input)
const hashBuffer = await crypto.subtle.digest('SHA-256', data)
return Array.from(new Uint8Array(hashBuffer))
.map(b => b.toString(16).padStart(2, '0'))
.join('')
},
})
```
### Hash Properties
- **Deterministic** – Same toolset always produces same hash
- **Order-independent** – Tool/property order doesn't affect hash
- **Change-sensitive** – Any definition change produces different hash
## Migrations
Migration syncs tool definitions to Neo4j, creating the graph structure for vector search.
### Basic Migration
```typescript
await rag.sync() // Migrates if needed
```
### Migration Flow
1. **Check** – Determines if migration needed (hash exists in Neo4j?)
2. **Index** – Creates vector index (1536 dimensions, cosine similarity)
3. **Embed** – Generates embeddings for tools, parameters, return types
4. **Store** – Creates graph structure in Neo4j
### Custom Migration Hooks
```typescript
const rag = createMCPRag({
// ...
migration: {
// Override migration check
shouldMigrate: async session => {
const result = await session.run('...')
return result.records.length === 0
},
// Custom migration logic
migrate: async (session, tools) => {
// Your migration code
},
// Intercept/modify migration statements
onBeforeMigrate: async statements => {
console.log('Migrating:', statements.length, 'statements')
return statements // Return modified or original
},
},
})
```
### Multi-Version Toolsets
Multiple toolset versions can coexist in Neo4j:
```typescript
// Version 1
await rag.sync()
const v1Hash = rag.getToolsetHash()
// Version 2 (both exist in DB)
rag.addTool('newTool', myTool)
await rag.sync()
const v2Hash = rag.getToolsetHash()
// Manage versions
const v1Info = await rag.getToolsetByHash(v1Hash)
await rag.deleteToolsetByHash(v1Hash) // Clean up old version
```
### Toolset Lifecycle
```typescript
// 1. Setup
const { hash } = await rag.sync()
// 2. Retrieve
const info = await rag.getToolsetByHash(hash)
// { hash, updatedAt, toolCount, tools: [...] }
// 3. Update
rag.addTool('newTool', tool)
await rag.sync() // Creates new version
// 4. Cleanup
await rag.deleteToolsetByHash(oldHash)
```
## License
MIT [rconnect.tech](https://www.rconnect.tech)