How to Use the Cloutly MCP in LlamaIndex

Q: How do I load Cloutly review data into a LlamaIndex vector store?

Use llama-index-tools-mcp to initialize the client, then call totoollistasync() to get the tools. Your agent can run listreviews to pull feedback and write it directly into your document index.

Q: Can I filter Cloutly reviews by location in my LlamaIndex pipeline?

Yes. Your agent can call listbusinesses to get the unique identifiers for each location, then pass those IDs to listreviews to build location-specific vector indexes.

Q: How does Cloutly help LlamaIndex agents avoid hallucinated replies?

The agent pulls the exact review text using listpendingreviews or searchreviewsbyauthor before drafting a reply. This provides ground-truth context, keeping the agent's output accurate.

Index live customer feedback into your LlamaIndex RAG pipeline using this MCP Server.

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Connect Cloutly MCP to LlamaIndex

Create your Vinkius account to connect Cloutly to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloutly with LlamaIndex

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Index live reviews for semantic search in LlamaIndex

This MCP Server lets your LlamaIndex agent fetch historical feedback via `list_reviews` and feed it directly into a vector index. Instead of querying a static database, your agent performs semantic search over live customer feedback to identify recurring product complaints or praise. By combining `list_businesses` with your vector store, you can build location-specific knowledge bases. The agent queries these indexes to give your team real-time answers grounded in actual customer experiences rather than outdated reports.

Ground agent responses in real-time Cloutly data

Prevent your agent from hallucinating by feeding it ground-truth data from the MCP Server. By calling `list_pending_reviews`, the agent checks the exact text of unanswered feedback before drafting a response, ensuring replies match the customer's specific issues. Use `search_reviews_by_author` to pull a customer's entire interaction history into the context window. Your agent can then synthesize past feedback to draft highly personalized replies using `reply_to_review`.

Context-aware review campaigns with LlamaIndex

Your agent can analyze customer sentiment from your index and decide when to trigger `send_review_invite`. It checks active campaigns using `list_campaigns` first to ensure the customer isn't already enrolled in an active outreach sequence. This setup lets you build intelligent outreach pipelines that only invite happy customers or follow up with unresolved cases. It bridges the gap between static customer documents and active review generation.

Setup guide

Set up Cloutly MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cloutly MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cloutly tools.",
)
response = await agent.run("List recent Cloutly data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cloutly. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cloutly MCP in LlamaIndex

Use `llama-index-tools-mcp` to initialize the client, then call `to_tool_list_async()` to get the tools. Your agent can run `list_reviews` to pull feedback and write it directly into your document index.

Yes. Your agent can call `list_businesses` to get the unique identifiers for each location, then pass those IDs to `list_reviews` to build location-specific vector indexes.

The agent pulls the exact review text using `list_pending_reviews` or `search_reviews_by_author` before drafting a reply. This provides ground-truth context, keeping the agent's output accurate.

Yes, the LlamaIndex integration supports async execution for all tools, allowing your agent to fetch reviews and send invites in parallel without blocking your main application loop.

Customer emails, phone numbers, and review text are processed inside secure, ephemeral V8 isolates on Vinkius. We do not persist any customer identifiers or review messages after the tool call completes.

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