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 LlamaIndex 9 tools — connect in under 2 minutes

Name: BrandMentions
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add BrandMentions as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 BrandMentions. "
            "You have 9 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in BrandMentions?"
    )
    print(response)

asyncio.run(main())

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About BrandMentions MCP Server

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

LlamaIndex agents combine BrandMentions tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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

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 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 BrandMentions to LlamaIndex via MCP

Follow these steps to integrate the BrandMentions 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 9 tools from BrandMentions

Why Use LlamaIndex with the BrandMentions MCP Server

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

Data-first architecture: LlamaIndex agents combine BrandMentions tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain BrandMentions tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query BrandMentions, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what BrandMentions tools were called, what data was returned, and how it influenced the final answer

BrandMentions + LlamaIndex Use Cases

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

Hybrid search: combine BrandMentions real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query BrandMentions 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 BrandMentions for fresh data

Analytical workflows: chain BrandMentions queries with LlamaIndex's data connectors to build multi-source analytical reports

BrandMentions MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect BrandMentions to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

BrandMentions + LlamaIndex FAQ

Common questions about integrating BrandMentions MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query BrandMentions tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.