Close MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Close 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 Close. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Close?"
)
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 Close MCP Server
Connect your Close CRM account to any AI agent and take full control of your sales automation through natural conversation. Streamline how you manage leads, deals, and daily to-dos natively.
LlamaIndex agents combine Close tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Lead Oversight — List and retrieve details for all leads (companies) including status and contact info natively
- Opportunity Intelligence — Access and monitor sales deals and their values across different pipelines flawlessly
- Task Management — List and review CRM tasks and reminders to stay on top of your follow-ups securely
- Pipeline Logistics — Monitor sales pipelines and understand where deals are in the funnel flawlessly
- Status Tracking — List available stages for leads and opportunities to maintain clean data flawlessly
- User Visibility — Access your own profile and core CRM metadata directly within your workspace flawlessly
The Close MCP Server exposes 8 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 Close to LlamaIndex via MCP
Follow these steps to integrate the Close 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 8 tools from Close
Why Use LlamaIndex with the Close MCP Server
LlamaIndex provides unique advantages when paired with Close through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Close tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Close tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Close, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Close tools were called, what data was returned, and how it influenced the final answer
Close + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Close MCP Server delivers measurable value.
Hybrid search: combine Close real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Close 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 Close for fresh data
Analytical workflows: chain Close queries with LlamaIndex's data connectors to build multi-source analytical reports
Close MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Close to LlamaIndex via MCP:
get_lead_details
Get detailed information for a specific lead
get_my_close_profile
Retrieve information about the authenticated user
get_opportunity_details
Get detailed information for a specific opportunity
list_close_leads
List all leads in Close CRM
list_close_opportunities
List all sales opportunities
list_close_pipelines
List sales pipelines configured in the account
list_close_tasks
List CRM tasks and reminders
list_lead_statuses
List available stages/statuses for leads
Example Prompts for Close in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Close immediately.
"List my last 5 leads in Close."
"Show me the value of my current sales pipeline."
"What are my pending tasks for this week?"
Troubleshooting Close MCP Server with LlamaIndex
Common issues when connecting Close to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpClose + LlamaIndex FAQ
Common questions about integrating Close 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 Close with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 Close to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.