Determ 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 Determ as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Determ. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Determ?"
)
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 Determ MCP Server
Integrate Determ (formerly Mediatoolkit), the powerful media monitoring and social listening platform, directly into your AI workflow. Track brand mentions across the web and social media, analyze sentiment trends, and monitor your competitive landscape using natural language.
LlamaIndex agents combine Determ tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Mention Monitoring — List and retrieve real-time media mentions for your keywords and topics from over 100 million sources.
- Sentiment Intelligence — Retrieve a breakdown of sentiment (positive, neutral, negative) for any of your monitoring queries.
- Query Management — List and review your configured monitoring queries and their specific settings.
- Analytics Reporting — Access metadata for your media monitoring and analytics reports directly via chat.
The Determ 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 Determ to LlamaIndex via MCP
Follow these steps to integrate the Determ 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 Determ
Why Use LlamaIndex with the Determ MCP Server
LlamaIndex provides unique advantages when paired with Determ through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Determ tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Determ tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Determ, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Determ tools were called, what data was returned, and how it influenced the final answer
Determ + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Determ MCP Server delivers measurable value.
Hybrid search: combine Determ real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Determ 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 Determ for fresh data
Analytical workflows: chain Determ queries with LlamaIndex's data connectors to build multi-source analytical reports
Determ MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Determ to LlamaIndex via MCP:
get_account_metadata
Retrieve settings and limits for your Determ account
get_mention_details
Get full content and technical metadata for a specific media mention
get_monitoring_query_details
Get detailed settings and status for a specific monitoring query
get_query_sentiment_summary
Retrieve a breakdown of sentiment (positive, neutral, negative) for a specific query
list_analytics_reports
List all available analytics and media monitoring reports
list_media_mentions
List recent media mentions for a specific monitoring query
list_monitoring_queries
List all media monitoring queries (keywords/topics) in your Determ account
list_recent_high_reach_mentions
List only the mentions with the highest estimated reach
list_top_media_sources
Identify the media sources with the highest volume of mentions (mock logic)
search_mentions_by_keyword
Search for specific keywords within the mentions of a monitoring query
Example Prompts for Determ in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Determ immediately.
"List my active monitoring queries."
"Show me the sentiment breakdown for the 'Main Competitor' query."
"What are the top media sources for 'Industry Trends'?"
Troubleshooting Determ MCP Server with LlamaIndex
Common issues when connecting Determ to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDeterm + LlamaIndex FAQ
Common questions about integrating Determ 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 Determ with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 Determ to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.