How to Use the Fera.ai MCP in LlamaIndex

Q: How do I connect the Fera.ai MCP Server to LlamaIndex?

Install the adapter using pip install llama-index-tools-mcp to get started. Initialize the BasicMCPClient with your Vinkius URL, convert the server tools using McpToolSpec, and pass them to your LlamaIndex agent.

Q: Can LlamaIndex index historical Fera.ai reviews?

Yes, your pipeline can call listreviews to pull historical feedback and chunk the text into your vector database. This lets you run semantic search queries across your entire customer review history.

Q: How does LlamaIndex prevent hallucinating Fera.ai ratings?

The agent uses getproductrating to fetch live, real-time metrics directly from the API. By grounding the response in this fresh data, the agent avoids making up review counts or star ratings.

Q: Can I restrict the MCP tools LlamaIndex can access?

Yes, you can use the allowedtools filter when setting up the client to restrict access to specific endpoints. For example, you can expose only getproductrating and block store metadata tools.

Q: How does Vinkius protect my Fera.ai review data?

All data processed from listreviews passes through an ephemeral V8 Isolate Sandbox. This zero-trust environment ensures your customer reviews and ratings are never cached or exposed to other tenants.

Index Fera.ai customer reviews and ratings directly into your LlamaIndex vector store for semantic search.

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Connect Fera.ai MCP to LlamaIndex

Create your Vinkius account to connect Fera.ai 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 Fera.ai with LlamaIndex

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Index customer feedback for semantic RAG

`list_reviews` retrieves raw customer feedback from your store so your LlamaIndex pipeline can convert the text into searchable vector embeddings. This tool lets you build a RAG system where your agent queries actual customer opinions to answer product questions. Instead of searching through static documents, your index stays fresh with live API data pulled directly from your Fera.ai account. The agent queries the vector store to find specific customer sentiments without relying on pre-baked datasets.

Query live product ratings with LlamaIndex

`get_product_rating` fetches the aggregated star counts and review numbers that your LlamaIndex agent needs to ground its answers in verified data. The agent combines this quantitative metric with your indexed product descriptions to prevent hallucinations about product quality. By using this MCP Server, your knowledge-augmented agent can verify the exact rating of an item before suggesting it to a buyer. This mechanism ensures your conversational search results always match the live reality of your storefront.

Search customer-submitted media files

`list_media` extracts the metadata of customer-submitted photos and videos, giving your LlamaIndex agent direct access to user-generated content. The agent indexes these media records to help users find visual proof of product quality during search queries. This MCP Server feeds live data into your pipeline so you can filter these resources by product or rating. This live data feed ensures your agent can recommend specific visual reviews to customers asking for real-world photos.

Setup guide

Set up Fera.ai 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 Fera.ai 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 Fera.ai tools.",
)
response = await agent.run("List recent Fera.ai 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 Fera.ai. 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 Fera.ai MCP in LlamaIndex

Install the adapter using `pip install llama-index-tools-mcp` to get started. Initialize the `BasicMCPClient` with your Vinkius URL, convert the server tools using `McpToolSpec`, and pass them to your LlamaIndex agent.

Yes, your pipeline can call `list_reviews` to pull historical feedback and chunk the text into your vector database. This lets you run semantic search queries across your entire customer review history.

The agent uses `get_product_rating` to fetch live, real-time metrics directly from the API. By grounding the response in this fresh data, the agent avoids making up review counts or star ratings.

Yes, you can use the `allowed_tools` filter when setting up the client to restrict access to specific endpoints. For example, you can expose only `get_product_rating` and block store metadata tools.

All data processed from `list_reviews` passes through an ephemeral V8 Isolate Sandbox. This zero-trust environment ensures your customer reviews and ratings are never cached or exposed to other tenants.

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