Redis Vector MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Redis Vector 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 Redis Vector. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Redis Vector?"
)
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 Redis Vector MCP Server
Connect your Redis database (equipped with the RediSearch module) to your AI agent, turning it into an advanced Vector Database administrator. Activating this integration grants your conversational interface the power to interact directly with your semantic search engine, enabling tasks like querying mathematical embeddings for similar records, configuring fresh vector indexes, and managing geometric data structures without needing dedicated external database clients.
LlamaIndex agents combine Redis Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Similarity Vector Search (KNN) — Let the AI perform rapid native vector comparisons (
search_vectors). Provide an embedding array via prompt or code, and retrieve the absolute nearesttop_kneighbors securely cached in your infrastructure. - Index Management — Actively discover all loaded RediSearch vector indexes, investigate their configured dimensions (
get_index_info), or command the AI to instantiate new KNN indexes (create_vector_index) tailored for fresh AI workloads. - Embedding Administration — Inject and modify geometric vector components associated with a document key (
upsert_vector), or purge legacy embeddings efficiently (delete_vector) to keep semantic records clean and operational.
The Redis Vector MCP Server exposes 6 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 Redis Vector to LlamaIndex via MCP
Follow these steps to integrate the Redis Vector 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 6 tools from Redis Vector
Why Use LlamaIndex with the Redis Vector MCP Server
LlamaIndex provides unique advantages when paired with Redis Vector through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Redis Vector tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Redis Vector tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Redis Vector, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Redis Vector tools were called, what data was returned, and how it influenced the final answer
Redis Vector + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Redis Vector MCP Server delivers measurable value.
Hybrid search: combine Redis Vector real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Redis Vector 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 Redis Vector for fresh data
Analytical workflows: chain Redis Vector queries with LlamaIndex's data connectors to build multi-source analytical reports
Redis Vector MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Redis Vector to LlamaIndex via MCP:
create_vector_index
Specify the name and vector dimensions. Creates a new RediSearch vector index
delete_vector
Deletes a vector document from Redis
get_index_info
Retrieves details for a specific vector index
list_indexes
Lists all RediSearch vector indexes
search_vectors
Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index
upsert_vector
Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash
Example Prompts for Redis Vector in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Redis Vector immediately.
"Search the index 'customer-support-vector' for the top 3 similar records to this embedding vector: [0.12, -0.45, 0.08, 0.99...]"
"Insert a new embedding into the database with the key 'user:439:preference' containing the vector `[0.2, -0.1...]`."
"Retrieve the index information logic and schema mapping for 'docs-semantic-index'."
Troubleshooting Redis Vector MCP Server with LlamaIndex
Common issues when connecting Redis Vector to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRedis Vector + LlamaIndex FAQ
Common questions about integrating Redis Vector 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 Redis Vector 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 Redis Vector to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.