# Mcp Rag

> A lightweight wrapper around AI SDK that intelligently indexes and retrieves MCP tools using graph-based vector search.

- **Type:** MCP server
- **Install:** `agentstack add mcp-rocket-connect-mcp-rag`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [rocket-connect](https://agentstack.voostack.com/s/rocket-connect)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [rocket-connect](https://github.com/rocket-connect)
- **Source:** https://github.com/rocket-connect/mcp-rag
- **Website:** https://www.rconnect.tech/blog/semantic-tool-discovery

## Install

```sh
agentstack add mcp-rocket-connect-mcp-rag
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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)

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [rocket-connect](https://github.com/rocket-connect)
- **Source:** [rocket-connect/mcp-rag](https://github.com/rocket-connect/mcp-rag)
- **License:** MIT
- **Homepage:** https://www.rconnect.tech/blog/semantic-tool-discovery

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-rocket-connect-mcp-rag
- Seller: https://agentstack.voostack.com/s/rocket-connect
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.