Fera.ai MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fera.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 Fera.ai. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Fera.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 Fera.ai MCP Server
Connect your Fera.ai account to any AI agent and take full control of your customer reviews, ratings, and user-generated content (UGC) through natural conversation.
LlamaIndex agents combine Fera.ai tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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 Orchestration — List all customer reviews and fetch detailed sentiment metadata for specific feedback natively
- Rating Intelligence — Query aggregated product ratings and review counts to analyze catalog performance flawlessly
- UGC Monitoring — List and inspect customer-submitted photos and videos to manage your visual social proof natively
- Customer Insights — Access detailed profiles of customers who have submitted feedback to personalize engagement synchronously
- Multi-Store Management — List and query data across all stores managed under your partner or business account flawlessly
- Integration Audit — Monitor active external integrations with platforms like Shopify, Wix, and BigCommerce directly from the cloud
- Identity Context — Verify your API secret key identity and account subscription details through the agent flawlessly
The Fera.ai MCP Server exposes 12 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 Fera.ai to LlamaIndex via MCP
Follow these steps to integrate the Fera.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 12 tools from Fera.ai
Why Use LlamaIndex with the Fera.ai MCP Server
LlamaIndex provides unique advantages when paired with Fera.ai through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fera.ai tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fera.ai tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fera.ai, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fera.ai tools were called, what data was returned, and how it influenced the final answer
Fera.ai + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fera.ai MCP Server delivers measurable value.
Hybrid search: combine Fera.ai real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fera.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 Fera.ai for fresh data
Analytical workflows: chain Fera.ai queries with LlamaIndex's data connectors to build multi-source analytical reports
Fera.ai MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Fera.ai to LlamaIndex via MCP:
get_account_info
Get Fera account and subscription details
get_customer
Get details for a specific customer profile
get_me
Get current API token identity info
get_product_rating
Get aggregated rating and review count for a product
get_review
Get details for a specific review
list_customers
List customers who have submitted feedback
list_external_integrations
List active external integrations (Shopify, etc.)
list_media
List customer-submitted photos and videos (UGC)
list_products
List products being tracked by Fera
list_reviews
List all customer reviews for your store
list_site_content
List social proof content and widgets
list_stores
List stores managed under your account
Example Prompts for Fera.ai in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Fera.ai immediately.
"List the latest 5 reviews for my store."
"Show me the average rating for product SKU-123."
"Check my active integrations on Fera."
Troubleshooting Fera.ai MCP Server with LlamaIndex
Common issues when connecting Fera.ai to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFera.ai + LlamaIndex FAQ
Common questions about integrating Fera.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 Fera.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 Fera.ai to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.