Froged MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Froged 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 Froged. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Froged?"
)
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 Froged MCP Server
Connect your Froged account to any AI agent to automate your customer success and support operations through the Model Context Protocol (MCP). Froged is an omnichannel customer service platform designed to improve retention and engagement. This MCP server enables you to track behavioral events, manage customer profiles, and participate in support conversations directly through natural conversation.
LlamaIndex agents combine Froged tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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.
Key Features
- Contact Management — List all customer profiles, fetch detailed metadata, and programmatically create or update contacts to maintain a 360-degree view.
- Behavioral Event Tracking — Access recent user events and post custom behavioral data (e.g., 'plan_upgraded') to trigger automated marketing campaigns.
- Support Conversations — List active support chats across all channels and post replies to conversations seamlessly.
- Marketing Campaigns — Retrieve a list of all active marketing and in-app campaigns to monitor engagement.
- Knowledge Base Access — Fetch published help articles from your Knowledge Base to aid in self-service support.
- Real-time Synchronization — Keep your customer success data and support inbox perfectly aligned with your internal tools.
The Froged MCP Server exposes 11 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 Froged to LlamaIndex via MCP
Follow these steps to integrate the Froged 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 11 tools from Froged
Why Use LlamaIndex with the Froged MCP Server
LlamaIndex provides unique advantages when paired with Froged through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Froged tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Froged tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Froged, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Froged tools were called, what data was returned, and how it influenced the final answer
Froged + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Froged MCP Server delivers measurable value.
Hybrid search: combine Froged real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Froged 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 Froged for fresh data
Analytical workflows: chain Froged queries with LlamaIndex's data connectors to build multi-source analytical reports
Froged MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Froged to LlamaIndex via MCP:
get_chat_details
Get conversation history
get_contact_details
Get contact metadata
list_behavioral_events
List tracked events
list_cs_contacts
List Froged contacts
list_kb_articles
List help articles
list_marketing_campaigns
List active campaigns
list_support_conversations
List support chats
send_chat_message
Send support reply
track_custom_event
Track user behavior
upsert_contact
Create/Update contact
verify_api_status
Verify API connection
Example Prompts for Froged in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Froged immediately.
"List my 5 most recent active support conversations."
"Track the event 'plan_upgraded' for user 'customer@email.com'."
"Show me the contact profile for 'jane@example.com'."
Troubleshooting Froged MCP Server with LlamaIndex
Common issues when connecting Froged to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFroged + LlamaIndex FAQ
Common questions about integrating Froged 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 Froged 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 Froged to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.