# How to integrate Crustdata MCP with LlamaIndex

```json
{
  "title": "How to integrate Crustdata MCP with LlamaIndex",
  "toolkit": "Crustdata",
  "toolkit_slug": "crustdata",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/crustdata/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/crustdata/framework/llama-index.md",
  "updated_at": "2026-05-12T10:07:51.622Z"
}
```

## Introduction

This guide walks you through connecting Crustdata to LlamaIndex using the Composio tool router. By the end, you'll have a working Crustdata agent that can find tech companies with recent funding milestones, enrich this lead's profile with latest data, list top decision makers in saas startups through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Crustdata account through Composio's Crustdata MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Crustdata with

- [OpenAI Agents SDK](https://composio.dev/toolkits/crustdata/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/crustdata/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/crustdata/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/crustdata/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/crustdata/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/crustdata/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/crustdata/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/crustdata/framework/cli)
- [Google ADK](https://composio.dev/toolkits/crustdata/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/crustdata/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/crustdata/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/crustdata/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/crustdata/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 Crustdata
- Connect LlamaIndex to the Crustdata MCP server
- Build a Crustdata-powered agent using LlamaIndex
- Interact with Crustdata 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 Crustdata MCP server, and what's possible with it?

The Crustdata MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Crustdata account. It provides structured and secure access to real-time company and people intelligence, so your agent can perform actions like lead enrichment, market research, investor portfolio analysis, and workforce trend tracking on your behalf.
- Comprehensive person and company enrichment: Instantly enrich leads or companies with up-to-date details for customer profiling, data verification, or targeted outreach.
- Advanced decision maker filtering: Find and analyze decision makers across organizations using complex filters, titles, and segmentation for your sales or marketing efforts.
- Investor portfolio and funding milestone analysis: Retrieve in-depth investor portfolio data, analyze funding milestones, and generate reports for investment research or deal sourcing.
- Workforce and job market trend insights: Fetch headcount and job listing timeseries data to track organizational growth, hiring activity, or competitive shifts in specific industries.
- Social and web activity monitoring: Collect and analyze LinkedIn posts and web traffic data for any company to assess engagement, sentiment, and digital footprint for market intelligence and outreach strategies.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CRUSTDATA_ENRICH_PERSON_SCREENER` | Enrich person screener | The screener_person_enrich endpoint enriches person data by providing additional information based on the given query. It allows users to retrieve detailed information about individuals, which can be useful for various purposes such as customer profiling, lead generation, or data verification. This endpoint should be used when you need to augment existing person data with additional details or verify information about a specific individual. The enrichment process draws from CrustData's extensive database and real-time data sources to provide up-to-date and comprehensive information. Users can customize the response by specifying the exact fields they need, optimizing data transfer and processing. Note that the availability and accuracy of enriched data may vary depending on the input provided and the information available in CrustData's systems. |
| `CRUSTDATA_FETCH_HEADCOUNT_BY_FACET_TIMESERIES` | Fetch headcount by facet timeseries | Retrieves headcount data as a timeseries with faceted analysis capabilities. This endpoint allows users to fetch detailed headcount information over time, applying complex filters, pagination, and sorting. It's particularly useful for HR analytics, workforce planning, and organizational growth analysis. The endpoint supports nested logical operations in its filtering mechanism, enabling highly specific queries. Users can paginate through large datasets and sort results based on multiple criteria. While powerful, this endpoint requires careful construction of the filters parameter to ensure accurate data retrieval. It should be used when detailed, time-based headcount analysis is needed, but may not be suitable for simple, non-time-series headcount queries or for real-time data needs due to its complexity. |
| `CRUSTDATA_FETCH_INVESTOR_PORTFOLIO_DATA` | Fetch investor portfolio data | Retrieves comprehensive investor portfolio data from the Data Lab section of the CrustData API. This endpoint provides access to detailed information about investor portfolios, including investment holdings, performance metrics, and other relevant data points. It is designed to support investment analysis, portfolio management, and decision-making processes in a B2B context. The endpoint should be used when detailed investor portfolio information is required for tasks such as investment screening, performance tracking, or generating analytical reports. It's important to note that this endpoint may not provide real-time data and the frequency of updates should be verified in the API documentation. Additionally, users should be aware of any data privacy and usage restrictions that may apply to the retrieved investor information. |
| `CRUSTDATA_FILTER_DECISION_MAKERS_DATA` | Filter decision makers data | Filters and retrieves decision maker data from the CrustData B2B SaaS integration platform based on complex criteria. This endpoint allows for advanced querying of decision maker information using a combination of filters, pagination, sorting, and title-based filtering. It's designed for scenarios where specific subsets of decision maker data need to be extracted or analyzed. The endpoint supports nested logical conditions in filters, enabling highly targeted data retrieval. Use this when you need to perform detailed analysis or reporting on decision makers across various organizations or industries. Note that the endpoint requires careful structuring of the request body to effectively utilize its advanced filtering capabilities. |
| `CRUSTDATA_POST_FUNDING_MILESTONE_TIME_SERIES_DATA` | Post funding milestone timeseries data | The FundingMilestoneTimeseries endpoint retrieves time-series data related to funding milestones for companies. It allows for complex querying of funding events over time, with flexible filtering, pagination, and sorting options. This endpoint is particularly useful for analyzing funding trends, comparing company funding histories, or tracking specific funding events across multiple organizations. The data returned is based on the specified filters and can be tailored to focus on particular time ranges, funding stages, or company characteristics. While it provides comprehensive funding milestone data, it does not include detailed company information beyond what's directly related to funding events. |
| `CRUSTDATA_POST_HEADCOUNT_TIMESERIES_DATA` | Post headcount timeseries data | Retrieves filtered and sorted headcount timeseries data from the CrustData Data Lab. This endpoint allows for complex querying of historical headcount information, enabling users to analyze workforce trends over time. It supports advanced filtering with nested conditions, pagination for handling large datasets, and customizable sorting. Ideal for generating reports, conducting workforce analysis, or integrating headcount data into third-party business intelligence tools. Note that the specifics of the returned data structure are not provided in the given schema. |
| `CRUSTDATA_POST_JOB_LISTINGS_TABLE_DATA` | Post job listings table data | This endpoint retrieves filtered and sorted job listings data for specified company tickers from a chosen dataset in the CrustData platform. It allows for highly customizable queries with complex filtering conditions, pagination, and sorting options. The endpoint is designed for bulk data retrieval and analysis of job market trends across multiple companies. Use this endpoint when you need to fetch and analyze job listing data for specific companies, apply custom filters to narrow down the results, or when you want to paginate through large sets of job data. It's particularly useful for market research, competitive analysis, or tracking employment trends in specific industries or companies. Note that this endpoint requires careful construction of the request body, especially for the filters parameter, which can support nested logical conditions. The performance and response time may vary depending on the complexity of the filters and the amount of data requested. |
| `CRUSTDATA_POST_WEB_TRAFFIC_DATA` | Post web traffic data | Retrieves filtered and sorted web traffic data from the CrustData platform. This endpoint allows for complex querying of web traffic information using nested conditions and logical operators. It supports pagination for handling large datasets and provides sorting capabilities for customized data presentation. Use this endpoint when you need to analyze web traffic patterns, filter data based on specific criteria, or extract insights from your web analytics. The endpoint is particularly useful for generating reports, identifying trends, or monitoring key performance indicators related to web traffic. |
| `CRUSTDATA_RETRIEVE_LINKED_IN_POSTS` | Retrieve linkedin posts | Retrieves LinkedIn posts for a specified company using CrustData's screener functionality. This endpoint allows users to gather social media data from LinkedIn, which can be used for analyzing company activity, engagement, and sentiment. It's particularly useful for B2B marketers, sales professionals, and analysts who need insights into a company's social media presence and content strategy. The endpoint supports filtering by date range and customizing the response fields, making it versatile for various use cases such as competitive analysis, lead generation, and market research. Note that the availability and completeness of data may depend on the company's LinkedIn activity and privacy settings. |
| `CRUSTDATA_SCREENER_COMPANY_INFORMATION` | Screener company information | The GetCompanyScreener endpoint allows users to search and filter companies based on various criteria such as headcount, growth rate, funding, and more. It provides a powerful way to identify specific companies that meet predefined conditions. This endpoint is particularly useful for tasks like lead generation, market research, and competitive analysis. The endpoint returns a list of companies matching the specified criteria, with each company entry containing key information such as name, industry, headcount, funding details, and growth metrics. Users can customize their search using multiple filters, sort the results, and paginate through large result sets. Note that the accuracy of the data depends on CrustData's real-time data collection and update frequency. |
| `CRUSTDATA_SCREEN_METRICS_AND_FILTER_CONDITIONS` | Screen metrics and filter conditions | The ScreenData endpoint enables advanced data screening and filtering on the CrustData platform. It allows users to construct complex queries for retrieving specific datasets based on custom metrics, filtering conditions, and sorting criteria. Use this endpoint for targeted data extraction, custom reporting, or data analysis within the B2B SaaS integration ecosystem. Note that while powerful, complex queries may impact performance with large datasets. |
| `CRUSTDATA_SEARCH_COMPANIES_WITH_FILTERS` | Search companies with filters | The CompanySearch endpoint enables users to search and filter companies using the CrustData API. It provides a powerful mechanism for querying company data based on multiple criteria, supporting complex filtering and pagination. This endpoint is ideal for applications that need to retrieve specific sets of company information, such as financial analysis tools, market research platforms, or business intelligence systems. The search functionality allows for precise data retrieval, enhancing the efficiency of data integration and analysis processes in B2B scenarios. Users should be aware that the endpoint requires careful construction of filter objects and proper use of pagination to ensure optimal performance and accurate results. |
| `CRUSTDATA_SEARCH_FOR_JOB_ID_IN_SCREENER` | Search for job id in screener | The screener_person_search endpoint allows users to search for persons associated with a specific job ID within the CrustData B2B SaaS integration platform. This POST request accepts a JSON payload containing a job_id and returns relevant person data linked to that job. It's particularly useful for scenarios where you need to quickly retrieve all individuals connected to a particular job or project. The endpoint is part of the platform's screening functionality, enabling efficient filtering of person records based on job-related criteria. While it provides a focused search based on job ID, it may not offer advanced filtering options or return comprehensive job details. |
| `CRUSTDATA_SEARCH_LINKED_IN_POSTS_BY_KEYWORD` | Search linkedin posts by keyword | This endpoint enables searching for LinkedIn posts using a specific keyword. It allows users to retrieve relevant content from LinkedIn by specifying a search term, along with options for pagination, sorting, and filtering by post date. The function is particularly useful for conducting market research, competitor analysis, or tracking industry trends on the LinkedIn platform. Users can fine-tune their search results by choosing how to sort the posts (by relevance or date) and selecting a specific time frame for the content. The endpoint returns paginated results, allowing for efficient navigation through large sets of matching posts. |

## Supported Triggers

None listed.

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

The Crustdata MCP server is an implementation of the Model Context Protocol that connects your AI agent to Crustdata. It provides structured and secure access so your agent can perform Crustdata 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 Crustdata account and project
- Basic familiarity with async Python/Typescript

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

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 Crustdata 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, crustdata)
- 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 Crustdata 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=["crustdata"],
    )

    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 Crustdata actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Crustdata 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: ["crustdata"],
    },
  );

  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 Crustdata 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 Crustdata
```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 Crustdata, 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=["crustdata"],
    )

    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 Crustdata actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Crustdata 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: ["crustdata"],
    },
  );

  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 Crustdata 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 Crustdata to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Crustdata 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 Crustdata MCP Agent with another framework

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

## Related Toolkits

- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active.
- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.
- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
- [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools.
- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.
- [Doppler marketing automation](https://composio.dev/toolkits/doppler_marketing_automation) - Doppler marketing automation is a platform for creating, sending, and tracking email campaigns. It helps you automate marketing workflows and manage subscriber lists for better engagement.

## Frequently Asked Questions

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

With a standalone Crustdata MCP server, the agents and LLMs can only access a fixed set of Crustdata tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Crustdata 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 Crustdata tools.

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

Yes, absolutely. You can configure which Crustdata 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 Crustdata data and credentials are handled as safely as possible.

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