Gong MCP Server for LlamaIndexGive LlamaIndex instant access to 14 tools to Check Gong Status, Get Call, Get Call Stats, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Gong 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 App Connector for LlamaIndex
The Gong app connector for LlamaIndex is a standout in the Sales Automation category — giving your AI agent 14 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Gong. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Gong?"
)
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 Gong MCP Server
Connect your Gong account to any AI agent and unlock conversation intelligence insights through natural conversation.
LlamaIndex agents combine Gong tool responses with indexed documents for comprehensive, grounded answers. Connect 14 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
- Call Management — List all recorded calls, filter by user or date range, and inspect individual call metadata with participants and topics
- Transcripts — Retrieve full speaker-labeled transcripts for any recorded call
- Team Analytics — List all team members with roles and activity metrics, and drill into per-user call statistics
- Scorecard Evaluation — Browse scoring rubrics and retrieve call quality scores for coaching
- Deal Pipeline — View deal stages, amounts, and associated calls for pipeline visibility
- Coaching Library — Access curated calls saved to the Gong coaching library for training
- Aggregate Statistics — Retrieve org-wide call metrics: total calls, duration, talk ratios
The Gong MCP Server exposes 14 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.
All 14 Gong tools available for LlamaIndex
When LlamaIndex connects to Gong through Vinkius, your AI agent gets direct access to every tool listed below — spanning revenue-intelligence, conversation-analysis, sales-coaching, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify connectivity
Get call details
Get call statistics
Get call transcript
Get user details
Get user stats
Get call scores
List calls
List calls by date range
List calls by user
List deals
List library calls
List scorecards
List users
Connect Gong to LlamaIndex via MCP
Follow these steps to wire Gong into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Gong MCP Server
LlamaIndex provides unique advantages when paired with Gong through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gong tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gong tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gong, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gong tools were called, what data was returned, and how it influenced the final answer
Gong + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gong MCP Server delivers measurable value.
Hybrid search: combine Gong real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gong 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 Gong for fresh data
Analytical workflows: chain Gong queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Gong in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Gong immediately.
"Show me all calls from this week and the transcript for the longest one."
"Compare the call performance of Sarah and Mike this quarter using their stats and scorecards."
"Show the deal pipeline and highlight deals that have had no calls in the last 2 weeks."
Troubleshooting Gong MCP Server with LlamaIndex
Common issues when connecting Gong to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGong + LlamaIndex FAQ
Common questions about integrating Gong MCP Server with LlamaIndex.