How to Use the Fxiaoke MCP in LlamaIndex
Index live CRM data into LlamaIndex vector stores using the Fxiaoke MCP Server to ground your sales questions.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Fxiaoke MCP to LlamaIndex
Create your Vinkius account to connect Fxiaoke to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Index Fxiaoke records into LlamaIndex vector stores
This MCP Server allows your LlamaIndex application to treat live Fxiaoke CRM data as a dynamic knowledge source. By running `list_leads` and `list_opportunities`, you can feed fresh Fxiaoke sales pipelines directly into your LlamaIndex vector index. Instead of relying on static CSV exports, your LlamaIndex RAG pipeline queries current Fxiaoke data. The LlamaIndex vector store updates dynamically, ensuring that questions about active Fxiaoke deals are always grounded in actual CRM records.
Ground RAG queries in live CRM context
When users ask about sales progress, LlamaIndex uses `list_sales_tasks` to pull the latest Fxiaoke updates. It bypasses LLM hallucinations by matching the user's query against raw Fxiaoke CRM task descriptions inside the LlamaIndex query engine. You can also pull Fxiaoke contact histories with `list_contacts` to give your LlamaIndex agent deep context. This ensures your generated summaries use correct Fxiaoke names and roles retrieved by LlamaIndex.
Query historical approvals with semantic search
Use `list_approvals` to pull past Fxiaoke workflow decisions and index them for LlamaIndex semantic search. When a sales rep asks why a past Fxiaoke discount was rejected, LlamaIndex searches the indexed approval text to find the exact reasoning. By calling `get_approval_details` on relevant Fxiaoke records, the system builds a LlamaIndex knowledge base of corporate policies. This lets your LlamaIndex agent answer compliance questions based on real historical Fxiaoke outcomes.
Set up Fxiaoke MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports. - 3
Convert to LlamaIndex tools
Call
mcp_tool_spec.to_tool_list_async()to convert all Fxiaoke MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
# Connect to the MCP
mcp_client = BasicMCPClient(
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)
# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()
# Create and run the agent
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt="You have access to Fxiaoke tools.",
)
response = await agent.run("List recent Fxiaoke data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fxiaoke. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Common questions about Fxiaoke MCP in LlamaIndex
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