How to Use the Dalil AI MCP in LlamaIndex

Q: How do I index Dalil AI company profiles into LlamaIndex?

You run listcompanies via the McpToolSpec and pass the resulting JSON objects to your Document parser. This creates vector embeddings of Middle Eastern corporate profiles that your query engine can search semantically.

Q: Can LlamaIndex retrieve historical Dalil AI contact signals?

Yes, by indexing the output of getcontactsignals into your vector store. Your agent queries this index to analyze historical behavioral trends of Middle Eastern buyers over time.

Q: Does the Dalil AI integration support LlamaIndex asynchronous MCP tool calls?

Yes, you can call totoollistasync() on the tool spec to load the Arabic business tools. This keeps your RAG pipeline responsive when fetching large volumes of company profiles via the MCP protocol.

Index native Arabic contact signals directly into your LlamaIndex vector store for grounded RAG.

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Create your Vinkius account to connect Dalil AI to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dalil AI with LlamaIndex

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Index Arabic buying signals for LlamaIndex RAG

This MCP Server uses `get_contact_signals` to fetch live regional buying patterns and convert them into indexable document nodes. LlamaIndex stores these nodes in your vector database, making real-time Middle Eastern market intelligence searchable. When your agent answers queries about regional accounts, it queries this index instead of guessing. You get answers grounded in fresh, culturally aware data rather than outdated training sets.

Build searchable directories of Middle Eastern companies

The `list_companies` tool extracts your tracked regional organizations directly into a structured LlamaIndex document store. This allows your RAG pipeline to map relationships between different entities operating in Gulf markets. Your query engine searches these documents using semantic similarity to find relevant companies. The system matches Arabic names and English transliterations accurately because the data is normalized at the server level.

Ground your agent in active LinkedIn outreach history

Your agent calls `list_contacts` to retrieve the latest interaction history and index it for context injection. Before composing a new message, LlamaIndex pulls this history to verify what was previously discussed. This setup prevents your agent from sending repetitive pitches via `send_linkedin_message`. The system reads the indexed logs first, ensuring every outbound message aligns with past touchpoints.

Setup guide

Set up Dalil AI MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Dalil AI MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Dalil AI tools.",
)
response = await agent.run("List recent Dalil AI data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dalil AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Dalil AI MCP in LlamaIndex

You run `list_companies` via the McpToolSpec and pass the resulting JSON objects to your Document parser. This creates vector embeddings of Middle Eastern corporate profiles that your query engine can search semantically.

Yes, by indexing the output of `get_contact_signals` into your vector store. Your agent queries this index to analyze historical behavioral trends of Middle Eastern buyers over time.

Yes, you can call `to_tool_list_async()` on the tool spec to load the Arabic business tools. This keeps your RAG pipeline responsive when fetching large volumes of company profiles via the MCP protocol.

The server outputs UTF-8 encoded Arabic strings that map directly to standard embedding models. This ensures your vector search queries match regional company profiles accurately.

All API data passes through an encrypted TLS tunnel directly to your local LlamaIndex instance. Vinkius operates under a zero-trust architecture, meaning your regional contact database is never cached or inspected.

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