Relevance AI 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 Relevance AI as an MCP tool provider through 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 Relevance AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Relevance AI?"
)
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 Relevance AI MCP Server
Connect your conversational AI to your Relevance AI workspace. By wrapping your custom agents, datasets, and API tools into this MCP extension, you transform your chat interface into a command center for orchestrating complex, autonomous AI operations and large-scale data workflows.
LlamaIndex agents combine Relevance AI 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
- Orchestrate Agents — Command your pre-built autonomous agents to execute tasks (
trigger_agent). Monitor their progress and read their exact reasoning steps dynamically (get_agent_run). Uselist_agentsto discover all available AI worker configurations. - Execute Tasks & Workflows — Trigger predefined chained prompts or specific micro-tasks without leaving your chat (
trigger_task), scaling repetitive workflows reliably. - Manage Knowledge Datasets — Take full control of your vector databases and tables. Insert new rows of knowledge directly from conversational context (
insert_documents), retrieve raw unstructured data entries (get_documents), or surgically delete obsolete knowledge base items (delete_documents).
The Relevance AI 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 Relevance AI to LlamaIndex via MCP
Follow these steps to integrate the Relevance AI 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 Relevance AI
Why Use LlamaIndex with the Relevance AI MCP Server
LlamaIndex provides unique advantages when paired with Relevance AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Relevance AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Relevance AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Relevance AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Relevance AI tools were called, what data was returned, and how it influenced the final answer
Relevance AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Relevance AI MCP Server delivers measurable value.
Hybrid search: combine Relevance AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Relevance AI 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 Relevance AI for fresh data
Analytical workflows: chain Relevance AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Relevance AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Relevance AI to LlamaIndex via MCP:
delete_documents
This action is irreversible. Deletes documents from a dataset by their IDs
get_agent_run
Retrieves the status and logs of a specific agent run
get_documents
Retrieves documents from a dataset
insert_documents
Provide documents as a JSON array of objects. Inserts documents into a dataset
list_agents
Lists all AI agents in the Relevance AI studio
list_datasets
Lists all datasets (knowledge tables) in the project
list_tasks
Lists all tasks (chained prompts) in the studio
list_tools
Lists all custom tools registered in the studio
trigger_agent
Provide inputs as a JSON object. Triggers an AI agent execution
trigger_task
Triggers a specific task execution
Example Prompts for Relevance AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Relevance AI immediately.
"List all available agents in my Relevance AI Studio and their IDs."
"Start a run for the 'Market Analysis' agent passing `{"company": "OpenAI"}` as the payload, then tell me the Run ID."
"Insert this JSON array of top competitor articles into the 'competitor_docs' dataset."
Troubleshooting Relevance AI MCP Server with LlamaIndex
Common issues when connecting Relevance AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRelevance AI + LlamaIndex FAQ
Common questions about integrating Relevance AI 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 Relevance AI 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 Relevance AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.