Can I perform a quick search for a brand name?

Yes! Use the `post_search` action to trigger an immediate query. The tool will return a search hash, which you can then use with `get_mentions` to read the results.

How do I see the influencers for my tracked project?

Simply ask the agent to `get_influencers` and provide the Project ID. It will return the list of top voices mentioning your brand.

Does creating a search consume my account API credits?

Yes, standard API billing applies. You can always use the `get_remaining_credits` tool to check your quota before running large queries.

BrandMentions MCP Server for LangChain 9 tools — connect in under 2 minutes

Name: BrandMentions
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 9 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect BrandMentions 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({
        "brandmentions": {
            "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 BrandMentions, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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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 BrandMentions MCP Server

Connect your BrandMentions social listening account to any AI agent and orchestrate your brand monitoring workflows through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with BrandMentions through native MCP adapters. Connect 9 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

On-the-Spot Searches — Trigger immediate web and social media searches for specific keywords and retrieve the results instantly.
Campaign Management — List all your active tracking projects or create new ones to continuously monitor your brand or competitors.
Mention Auditing — Retrieve detailed mentions and sentiment analysis for your ongoing projects.
Influencer Discovery — List key influencers associated with your tracked keywords and projects.
Credit Tracking — Check your API limits and remaining credits in real-time.

The BrandMentions MCP Server exposes 9 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 BrandMentions to LangChain via MCP

Follow these steps to integrate the BrandMentions 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 9 tools from BrandMentions via MCP

Why Use LangChain with the BrandMentions MCP Server

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

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

BrandMentions + LangChain Use Cases

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

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

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

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

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

BrandMentions MCP Tools for LangChain (9)

These 9 tools become available when you connect BrandMentions to LangChain via MCP:

add_project

Create a new project for daily tracking

delete_project

Delete a project

get_influencers

List influencers for a specific project

get_mentions

Get full results for a completed search job

get_processed_mentions

Get partial results for a running search job

get_project_mentions

Retrieve mentions for a specific project

get_remaining_credits

Get current API credits limit/usage

list_projects

List all active campaigns/projects

post_search

Start an on-the-spot search job

Example Prompts for BrandMentions in LangChain

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

"List all active tracking projects in BrandMentions."

"Start a quick search for the keyword 'Vinkius'."

"Show me the top influencers for project proj_1."

Troubleshooting BrandMentions MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

BrandMentions + LangChain FAQ

Common questions about integrating BrandMentions 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.