Okendo Reviews 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 Okendo Reviews 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 Okendo Reviews. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Okendo Reviews?"
)
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 Okendo Reviews MCP Server
Connect your Okendo account to your AI agent and gain deep insights into your customer feedback and social proof through natural conversation.
LlamaIndex agents combine Okendo Reviews 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
- Review Monitoring — List and inspect customer reviews, including star ratings, titles, and body content.
- Q&A Oversight — Access customer questions and their respective answers to identify common product concerns.
- Aggregate Ratings — Retrieve overall store and product-level star ratings and review counts.
- Product Intelligence — View all products tracked in Okendo and access their specific review metrics.
- Media Access — List photos and videos uploaded by customers as part of their reviews.
- Store Settings — Retrieve high-level configuration and display settings for your Okendo instance.
- Deep Inspection — Fetch complete metadata for specific reviews or questions using their unique IDs.
The Okendo Reviews 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 Okendo Reviews to LlamaIndex via MCP
Follow these steps to integrate the Okendo Reviews 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 Okendo Reviews
Why Use LlamaIndex with the Okendo Reviews MCP Server
LlamaIndex provides unique advantages when paired with Okendo Reviews through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Okendo Reviews tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Okendo Reviews tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Okendo Reviews, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Okendo Reviews tools were called, what data was returned, and how it influenced the final answer
Okendo Reviews + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Okendo Reviews MCP Server delivers measurable value.
Hybrid search: combine Okendo Reviews real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Okendo Reviews 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 Okendo Reviews for fresh data
Analytical workflows: chain Okendo Reviews queries with LlamaIndex's data connectors to build multi-source analytical reports
Okendo Reviews MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Okendo Reviews to LlamaIndex via MCP:
get_aggregate_ratings
Get store aggregate ratings
get_okendo_product_details
Get product review info
get_question_details
Get specific question details
get_review_details
Get specific review details
get_store_settings
Get Okendo store settings
list_customer_questions
List customer Q&A questions
list_okendo_products
List products tracked in Okendo
list_question_answers
List answers for a question
list_review_media
List customer-uploaded media
list_reviews
List customer reviews
Example Prompts for Okendo Reviews in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Okendo Reviews immediately.
"Show me the 5 most recent customer reviews."
"What is the overall star rating for our store?"
"List the recent questions asked by customers on the site."
Troubleshooting Okendo Reviews MCP Server with LlamaIndex
Common issues when connecting Okendo Reviews to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOkendo Reviews + LlamaIndex FAQ
Common questions about integrating Okendo Reviews 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 Okendo Reviews with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
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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 Okendo Reviews to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.