Dashdoc MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dashdoc as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dashdoc. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Dashdoc?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Dashdoc MCP Server
Integrate Dashdoc, the leading transport management system (TMS), directly into your AI workflow. Manage your transport orders, monitor your fleet of trucks and trailers, and track delivery addresses using natural language.
LlamaIndex agents combine Dashdoc tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Transport Management — List and retrieve detailed information for all your transport orders and their statuses.
- Fleet Monitoring — Track your trucks, trailers, and drivers registered in the Dashdoc system.
- Address Book — Manage delivery and pickup addresses and create new records instantly.
- Partner Insights — List contacts and business partners associated with your transport operations.
The Dashdoc MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Dashdoc to LlamaIndex via MCP
Follow these steps to integrate the Dashdoc MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Dashdoc
Why Use LlamaIndex with the Dashdoc MCP Server
LlamaIndex provides unique advantages when paired with Dashdoc through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dashdoc tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dashdoc tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dashdoc, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dashdoc tools were called, what data was returned, and how it influenced the final answer
Dashdoc + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dashdoc MCP Server delivers measurable value.
Hybrid search: combine Dashdoc real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dashdoc to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dashdoc for fresh data
Analytical workflows: chain Dashdoc queries with LlamaIndex's data connectors to build multi-source analytical reports
Dashdoc MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Dashdoc to LlamaIndex via MCP:
create_new_address
Persists site metadata including name, city, and postal code, returning the newly generated system ID for the address. Add a new address to your Dashdoc address book
get_my_user_info
Returns account-level metadata including user ID, role, and associated fleet/company configuration. Retrieve metadata for the current authenticated user
get_transport_details
Resolves internal IDs to human-readable names, including full site addresses, contact phone numbers, specific cargo items, and historical status logs. Get detailed information for a specific transport order
list_fleet_drivers
Returns driver profiles including internal identifiers, professional names, and link to associated vehicle units. List all drivers registered in the system
list_fleet_trailers
Returns metadata such as trailer type (e.g., refrigerated, tautliner), registration numbers, and fleet assignment status. List all trailers in your fleet
list_fleet_trucks
Includes license plates, vehicle types, maximum load capacity, and current operational status. List all trucks in your fleet
list_saved_addresses
Returns a collection of site objects with GPS coordinates, technical contact details, and site-specific instructions (e.g., gate codes, loading bay requirements). List all saved delivery and pickup addresses
list_transport_contacts
Resolves business partner identities, including legal names, tax identifiers, and primary communication channels for logistics coordination. List contacts and business partners
list_transports
Returns transport metadata including status (e.g., requested, confirmed, ongoing, done), pickup/delivery references, customer IDs, and scheduling timestamps. List all transport orders in Dashdoc
search_transports_by_reference
Matches the provided reference keyword against transport-level identifiers and customer references using case-insensitive partial matching. Search for transport orders by reference keyword
Example Prompts for Dashdoc in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Dashdoc immediately.
"List all transport orders that are 'Ongoing'."
"Show me the details for transport order 'TR123'."
"List all trucks in our fleet."
Troubleshooting Dashdoc MCP Server with LlamaIndex
Common issues when connecting Dashdoc to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDashdoc + LlamaIndex FAQ
Common questions about integrating Dashdoc MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Dashdoc with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Dashdoc to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.