How to Use the Dashdoc MCP in LlamaIndex

Q: How do I start indexing Dashdoc data with LlamaIndex?

First, install the llama-index-tools-mcp package and point the BasicMCPClient to your Vinkius endpoint. Use McpToolSpec to get the list of Dashdoc tools. Then you can use these tools in a FunctionAgent to fetch and index data.

Q: Can my LlamaIndex app query past Dashdoc transport orders?

Absolutely. That's the core idea. You run the listtransports tool periodically and feed the results into a LlamaIndex vector store. Your application then queries that index, not the live API, for historical questions.

Turn your Dashdoc transport history into a queryable knowledge base with LlamaIndex. Ask questions, get answers from your own data.

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Connect Dashdoc MCP to LlamaIndex

Create your Vinkius account to connect Dashdoc 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 Dashdoc with LlamaIndex

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Index your transport data for semantic search

Stop digging through dashboards. Use LlamaIndex to run `list_transports` and `get_transport_details` for all your orders, then index the results into a vector store. This creates a searchable history of your entire operation. Now you can ask plain-English questions like, "What were the last five transports we did for customer X?" or "Show me all ongoing refrigerated transports." LlamaIndex will query your indexed Dashdoc data to give you a precise answer, grounded in facts, not guesses.

Build a knowledge base of your fleet and sites

Your agent can build and maintain a complete inventory of your assets. It periodically calls `list_fleet_trucks`, `list_fleet_trailers`, and `list_fleet_drivers`, indexing the output. The same goes for your address book, using `list_saved_addresses`. With this knowledge base, you can ask complex questions that cross-reference data. For example: "Which drivers are available and certified for a tautliner trailer near the Springfield depot?" Your RAG application finds the answer by searching the indexed data from multiple Dashdoc tools.

Query contacts and references with this MCP Server

This MCP Server lets you index more than just orders. Your LlamaIndex agent can run `list_transport_contacts` to build a queryable directory of your business partners, including their legal names and tax info. It can also index orders by customer reference numbers from `search_transports_by_reference`. This means you can ask things like, "What's the main contact for our partner with tax ID Y?" or "Find all orders with 'URGENT' in the reference." Your agent gets the answer directly from the indexed Dashdoc API responses, ensuring it's always up to date.

Setup guide

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

First, install the `llama-index-tools-mcp` package and point the `BasicMCPClient` to your Vinkius endpoint. Use `McpToolSpec` to get the list of Dashdoc tools. Then you can use these tools in a `FunctionAgent` to fetch and index data.

Absolutely. That's the core idea. You run the `list_transports` tool periodically and feed the results into a LlamaIndex vector store. Your application then queries that index, not the live API, for historical questions.

LlamaIndex is designed for this. You can create one index from your internal PDFs or documents and another from the Dashdoc MCP tool outputs. A query engine can then search across both indexes to answer questions with data from all sources.

You'll need to re-index the data to keep your knowledge base fresh. You can set up a process where your LlamaIndex application periodically calls tools like `list_transports` and updates the corresponding entries in your vector store.

The connection between LlamaIndex and your Dashdoc MCP Server is secure. Data passed during indexing, such as transport manifests or driver information, is sent over an encrypted channel. Vinkius processes these calls in a zero-trust, single-use sandbox.

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