How to Use the Dashly MCP in LlamaIndex

Q: How do I connect LlamaIndex to my Dashly account?

Install llama-index-tools-mcp and create a BasicMCPClient with your Vinkius URL. Then, wrap it in the McpToolSpec to generate tools for your agent.

Q: Can I choose which Dashly tools my LlamaIndex agent can use?

Yes. The McpToolSpec supports an allowedtools filter. You can give an agent read-only access by only allowing tools like getconversation and listusers.

Q: How does indexing Dashly data with LlamaIndex actually work?

Your agent calls a tool, like listconversations. LlamaIndex takes the JSON output and feeds it into an ingestion pipeline, creating vector embeddings that capture the meaning of the text for later searches.

Q: Can a LlamaIndex agent update a user's profile in Dashly?

Absolutely. If you give the agent access to the setuserprops tool, it can modify user data. For example, it could analyze a conversation and then tag a user as a 'churnrisk'.

Turn your Dashly customer conversations and user data into a searchable knowledge base for your LlamaIndex RAG agent.

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Connect Dashly MCP to LlamaIndex

Create your Vinkius account to connect Dashly 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 Dashly with LlamaIndex

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Index Live Customer Support Data

This server exposes your Dashly account as a set of queryable tools. Your LlamaIndex agent can call `list_conversations` and `list_users` to fetch raw data directly from the source. Instead of just using the data once, LlamaIndex ingests it into a vector index. This turns your real-time support activity into a knowledge base your agent can search through later.

Ground Agent Answers in Real Data

Stop agent hallucinations. When you ask a question, your LlamaIndex agent queries the indexed Dashly data first. It can find patterns across thousands of conversations. This means answers are based on what's actually happening with your users. The agent can synthesize information from `get_user_details` and `get_conversation` to explain trends or find specific examples.

An MCP Server That Takes Action

LlamaIndex isn't just for reading. After querying its index for context, your agent can use the write tools in this MCP server to act on its findings. For instance, after identifying a common problem, the agent could proactively `send_reply` to affected users. Or it could tag users with `set_user_props` for a follow-up campaign.

Setup guide

Set up Dashly 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 Dashly 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 Dashly tools.",
)
response = await agent.run("List recent Dashly 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 Dashly. 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 Dashly MCP in LlamaIndex

Install `llama-index-tools-mcp` and create a `BasicMCPClient` with your Vinkius URL. Then, wrap it in the `McpToolSpec` to generate tools for your agent.

Yes. The `McpToolSpec` supports an `allowed_tools` filter. You can give an agent read-only access by only allowing tools like `get_conversation` and `list_users`.

Your agent calls a tool, like `list_conversations`. LlamaIndex takes the JSON output and feeds it into an ingestion pipeline, creating vector embeddings that capture the meaning of the text for later searches.

Absolutely. If you give the agent access to the `set_user_props` tool, it can modify user data. For example, it could analyze a conversation and then tag a user as a 'churn_risk'.

The MCP server provides secure, direct transport of Dashly data, like user profiles and chat logs, to your LlamaIndex application. You control where the data is indexed and stored—it's your vector database, on your infrastructure. Vinkius doesn't see or keep it.

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