# How to integrate Rocketadmin MCP with LlamaIndex

```json
{
  "title": "How to integrate Rocketadmin MCP with LlamaIndex",
  "toolkit": "Rocketadmin",
  "toolkit_slug": "rocketadmin",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/rocketadmin/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/rocketadmin/framework/llama-index.md",
  "updated_at": "2026-03-29T06:48:20.110Z"
}
```

## Introduction

This guide walks you through connecting Rocketadmin to LlamaIndex using the Composio tool router. By the end, you'll have a working Rocketadmin agent that can list all records in orders table, update user email in rocketadmin database, delete inactive users from customers table through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Rocketadmin account through Composio's Rocketadmin MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Rocketadmin with

- [OpenAI Agents SDK](https://composio.dev/toolkits/rocketadmin/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/rocketadmin/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/rocketadmin/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/rocketadmin/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/rocketadmin/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/rocketadmin/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/rocketadmin/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/rocketadmin/framework/cli)
- [Google ADK](https://composio.dev/toolkits/rocketadmin/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/rocketadmin/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/rocketadmin/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/rocketadmin/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/rocketadmin/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for Rocketadmin
- Connect LlamaIndex to the Rocketadmin MCP server
- Build a Rocketadmin-powered agent using LlamaIndex
- Interact with Rocketadmin through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## What is the Rocketadmin MCP server, and what's possible with it?

The Rocketadmin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Rocketadmin account. It provides structured and secure access so your agent can perform Rocketadmin operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ROCKETADMIN_CHECK_API_KEY` | Check API Key | Tool to validate whether an API key is legitimate and active. Use when you need to verify that the current API key is valid before performing other operations. |
| `ROCKETADMIN_DELETE_TABLE_ROW` | Delete Table Row by Primary Key | Tool to delete a single row from a database table by primary key. Use when you need to remove a specific row identified by its ID. This is an API+ feature. |
| `ROCKETADMIN_GET_COMPANY_INVITE_VERIFY` | Verify Company Invitation Link | Tool to check if a company invitation verification link is available and valid. Use when you need to verify a company invitation token before accepting the invitation. |
| `ROCKETADMIN_GET_CONNECTIONS` | Get All Connections | Tool to retrieve all database connections where the user has access. Use when you need to discover available connections in the user's workspace. |
| `ROCKETADMIN_GET_CONNECTION_TABLES` | Get Connection Tables | Tool to retrieve all tables from a database connection. Use when you need to discover available tables in a specific connection. |
| `ROCKETADMIN_GET_CONNECTION_TABLES_V2` | Get Connection Tables V2 | Tool to retrieve all tables in a database connection organized with category information. Use when you need to discover available tables in a specific connection. |
| `ROCKETADMIN_VALIDATE_CONNECTION_TOKEN` | Validate Connection Token | Tool to validate if connection agent token is valid. Use when you need to check if the current connection token is still authorized and active. |
| `ROCKETADMIN_GET_HELLO` | Get Hello | Tool to retrieve a hello greeting message from the Rocketadmin API. Use when testing API connectivity or getting a simple greeting response. |
| `ROCKETADMIN_GET_SAAS_USERS_BY_EMAIL` | Get SaaS Users by Email | Tool to retrieve user information by email address. Use when you need to get details about a specific user by their email. |
| `ROCKETADMIN_GET_TABLE_ROW_BY_PRIMARY_KEY` | Get Table Row by Primary Key | Tool to retrieve a single row from a database table using its primary key. Use when you need to fetch specific row data by its ID from a RocketAdmin connection. |
| `ROCKETADMIN_GET_ALL_TABLE_ROWS` | Get All Table Rows | Tool to retrieve all rows from a database table with support for pagination, filtering, and sorting. Use when you need to fetch multiple rows from a RocketAdmin connection table. |
| `ROCKETADMIN_GET_TABLE_STRUCTURE` | Get Table Structure | Tool to retrieve the structural information of a database table including columns, data types, constraints, and relationships. Use when you need to understand the schema of a specific table in a RocketAdmin connection. |
| `ROCKETADMIN_VERIFY_USER_EMAIL` | Verify User Email | Tool to verify a user's email address using a verification token. Use when you need to confirm a user's email address after registration or email change. |
| `ROCKETADMIN_EXPORT_TABLE_AS_CSV` | Export Table as CSV | Tool to export table data as a CSV file from RocketAdmin. Use when you need to download table data in CSV format. This is an API+ feature that exports the specified table with optional filtering, pagination, and sorting. |
| `ROCKETADMIN_ADD_ROW_TO_TABLE` | Add Row to Table | Tool to add a new row to a database table in RocketAdmin. Use when you need to insert data into a specific table. This is an API+ feature that creates a new row with the provided field values. |
| `ROCKETADMIN_FIND_TABLE_ROWS_WITH_FILTERS` | Find Table Rows with Filters | Tool to retrieve all rows from a database table with filter parameters in the request body. Use when you need to fetch rows with complex filtering conditions. This is an API+ feature that supports advanced filtering with operators like equals, greater than, less than, like, in, etc. |
| `ROCKETADMIN_UPDATE_TABLE_ROW_BY_PRIMARY_KEY` | Update Table Row by Primary Key | Tool to update a row in a database table by its primary key. Use when you need to modify existing row data in a RocketAdmin connection. This is an API+ feature. |
| `ROCKETADMIN_DELETE_MULTIPLE_TABLE_ROWS` | Delete Multiple Table Rows | Tool to delete multiple rows from a table by primary key. Use when you need to batch delete rows identified by their primary keys. This is an API+ feature. |
| `ROCKETADMIN_UPDATE_MULTIPLE_TABLE_ROWS` | Update Multiple Table Rows | Tool to update multiple rows in a table by primary key. Use when you need to batch update rows identified by their primary keys with the same new values. This is an API+ feature. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Rocketadmin MCP server is an implementation of the Model Context Protocol that connects your AI agent to Rocketadmin. It provides structured and secure access so your agent can perform Rocketadmin operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A Rocketadmin account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Rocketadmin

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for Rocketadmin access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, rocketadmin)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available Rocketadmin tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["rocketadmin"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Rocketadmin actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Rocketadmin actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["rocketadmin"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Rocketadmin actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to Rocketadmin
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Rocketadmin, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["rocketadmin"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Rocketadmin actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Rocketadmin actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["rocketadmin"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Rocketadmin actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Rocketadmin to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Rocketadmin tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## How to build Rocketadmin MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/rocketadmin/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/rocketadmin/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/rocketadmin/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/rocketadmin/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/rocketadmin/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/rocketadmin/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/rocketadmin/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/rocketadmin/framework/cli)
- [Google ADK](https://composio.dev/toolkits/rocketadmin/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/rocketadmin/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/rocketadmin/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/rocketadmin/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/rocketadmin/framework/crew-ai)

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- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
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- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
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- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Rocketadmin MCP?

With a standalone Rocketadmin MCP server, the agents and LLMs can only access a fixed set of Rocketadmin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Rocketadmin and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Rocketadmin tools.

### Can I manage the permissions and scopes for Rocketadmin while using Tool Router?

Yes, absolutely. You can configure which Rocketadmin scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Rocketadmin data and credentials are handled as safely as possible.

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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)