How do I find my Marchex Client ID and Secret?

Log in to the Marchex Developer Console and create an application to receive your credentials.

What call data can I access?

You can access call metadata, duration, caller info, status, and aggregated performance metrics.

Is my integration secure?

Absolutely. Your credentials are encrypted at rest and injected securely at runtime.

Marchex MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Marchex
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Marchex through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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({
        "marchex": {
            "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 Marchex, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Marchex MCP Server

Connect your Marchex account to any AI agent and take full control of your conversation intelligence through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Marchex 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

Call Tracking — Search for calls, fetch detailed metadata, and monitor statuses
Campaign Management — List and inspect tracking campaigns and their configurations
Number Management — Manage tracking phone numbers and account structures
Analytics — Retrieve aggregated performance metrics and call analytics

The Marchex 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 Marchex to LangChain via MCP

Follow these steps to integrate the Marchex 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 Marchex via MCP

Why Use LangChain with the Marchex MCP Server

LangChain provides unique advantages when paired with Marchex through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Marchex 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 Marchex queries for multi-turn workflows

Marchex + LangChain Use Cases

Practical scenarios where LangChain combined with the Marchex MCP Server delivers measurable value.

RAG with live data: combine Marchex tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Marchex, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Marchex tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Marchex tool call, measure latency, and optimize your agent's performance

Marchex MCP Tools for LangChain (10)

These 10 tools become available when you connect Marchex to LangChain via MCP:

get_account_details

Get details for a specific account

get_call_analytics

Get call analytics

get_call_details

Get details for a specific call

get_campaign_details

Get details for a specific campaign

get_number_details

Get details for a specific number

list_accounts

List all accounts

list_campaigns

List all campaigns

list_numbers

List all tracking numbers

list_users

List all users

search_calls

Search for phone calls

Example Prompts for Marchex in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Marchex immediately.

"Search for calls from last week."

"List active tracking campaigns."

"Show call analytics for today."

Troubleshooting Marchex MCP Server with LangChain

Common issues when connecting Marchex to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Marchex + LangChain FAQ

Common questions about integrating Marchex MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use 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?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.