Dashdoc MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Dashdoc through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"dashdoc": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Dashdoc, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Dashdoc through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Dashdoc MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Dashdoc via MCP
Why Use LangChain with the Dashdoc MCP Server
LangChain provides unique advantages when paired with Dashdoc through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Dashdoc MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Dashdoc queries for multi-turn workflows
Dashdoc + LangChain Use Cases
Practical scenarios where LangChain combined with the Dashdoc MCP Server delivers measurable value.
RAG with live data: combine Dashdoc tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Dashdoc, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Dashdoc tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Dashdoc tool call, measure latency, and optimize your agent's performance
Dashdoc MCP Tools for LangChain (10)
These 10 tools become available when you connect Dashdoc to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Dashdoc to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersDashdoc + LangChain FAQ
Common questions about integrating Dashdoc MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.