Mention 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 Mention as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Mention. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Mention?"
)
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 Mention MCP Server
Connect your Mention account to any AI agent and take full control of your social monitoring and brand alerts through natural conversation.
LlamaIndex agents combine Mention 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
- Alert Management — List all active monitoring alerts and fetch detailed configuration metadata
- Mention Tracking — Retrieve recent social media mentions, filter for favorites, and search by text
- Deep Inspection — Fetch full content, metadata, and sentiment analysis for specific mentions
- Brand Analytics — Access volume and sentiment statistics for your monitoring alerts instantly
- Account Visibility — List authorized users and connected external social media accounts
The Mention 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 Mention to LlamaIndex via MCP
Follow these steps to integrate the Mention 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 Mention
Why Use LlamaIndex with the Mention MCP Server
LlamaIndex provides unique advantages when paired with Mention through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mention tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mention tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mention, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mention tools were called, what data was returned, and how it influenced the final answer
Mention + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mention MCP Server delivers measurable value.
Hybrid search: combine Mention real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mention 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 Mention for fresh data
Analytical workflows: chain Mention queries with LlamaIndex's data connectors to build multi-source analytical reports
Mention MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Mention to LlamaIndex via MCP:
get_account_info
Get account information
get_alert
Get details for a specific alert
get_alert_statistics
Get statistics for an alert
get_mention_details
Get details for a specific mention
list_account_users
List users associated with the account
list_alerts
List all monitoring alerts
list_connected_external_accounts
) linked. List connected social accounts
list_favorite_mentions
List favorite mentions for an alert
list_mentions
List mentions for an alert
search_mentions
Search mentions by text
Example Prompts for Mention in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mention immediately.
"List all active alerts in my Mention account."
"Search mentions for 'artificial intelligence' in alert ID 123."
"Show volume statistics for my primary brand alert."
Troubleshooting Mention MCP Server with LlamaIndex
Common issues when connecting Mention to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMention + LlamaIndex FAQ
Common questions about integrating Mention 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 Mention 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 Mention to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.