Freshsuccess 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 Freshsuccess as an MCP tool provider through the 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 Freshsuccess. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Freshsuccess?"
)
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 Freshsuccess MCP Server
Connect your Freshsuccess (Freshdesk Customer Success) account to any AI agent to automate your customer retention and engagement operations through the Model Context Protocol (MCP). Freshsuccess empowers Customer Success Managers (CSMs) to prevent churn, increase expansion revenue, and proactively manage accounts. This MCP server enables you to track health scores, update user metadata, and log custom metrics directly through natural conversation.
LlamaIndex agents combine Freshsuccess tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the 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
- Account Oversight — List all customer accounts, retrieve detailed profiles including health scores, and map assigned CSMs instantly.
- User & Engagement Tracking — Access detailed end-user profiles, monitor product usage, and upsert records to ensure accurate data.
- Proactive Alerts — Monitor configured customer success alerts (e.g., drop in usage, poor health) to prioritize interventions.
- Task Management — Retrieve pending CSM tasks and to-dos to keep your team aligned on retention efforts.
- Custom Metric Logging — Post specific product usage values or custom metrics directly to accounts and users to influence health scoring.
- Data Synchronization — Ensure your CRM and CS platforms are perfectly aligned by automating record updates.
The Freshsuccess 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 Freshsuccess to LlamaIndex via MCP
Follow these steps to integrate the Freshsuccess 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 Freshsuccess
Why Use LlamaIndex with the Freshsuccess MCP Server
LlamaIndex provides unique advantages when paired with Freshsuccess through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Freshsuccess tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Freshsuccess tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Freshsuccess, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Freshsuccess tools were called, what data was returned, and how it influenced the final answer
Freshsuccess + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Freshsuccess MCP Server delivers measurable value.
Hybrid search: combine Freshsuccess real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Freshsuccess 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 Freshsuccess for fresh data
Analytical workflows: chain Freshsuccess queries with LlamaIndex's data connectors to build multi-source analytical reports
Freshsuccess MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Freshsuccess to LlamaIndex via MCP:
check_api_status
Verify API connection
get_account_health
Get account metadata
get_user_health
Get user metadata
list_cs_accounts
List customer accounts
list_cs_alerts
g. drop in usage). List active alerts
list_cs_tasks
List pending tasks
list_cs_users
List account users
list_custom_metrics
List defined metrics
post_metric_value
Record custom metric
upsert_cs_account
Create/Update account
upsert_cs_user
Create/Update user
Example Prompts for Freshsuccess in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Freshsuccess immediately.
"List all active customer success alerts."
"Show me the health score for account 'acc_123'."
"Post a custom metric 'api_calls' with value 150 for user 'user_987'."
Troubleshooting Freshsuccess MCP Server with LlamaIndex
Common issues when connecting Freshsuccess to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFreshsuccess + LlamaIndex FAQ
Common questions about integrating Freshsuccess 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 Freshsuccess 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 Freshsuccess to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.