How to Use the Jiminny MCP in LlamaIndex

Turn your Jiminny conversation intelligence into a searchable knowledge base for LlamaIndex.

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Connect Jiminny MCP to LlamaIndex

Create your Vinkius account to connect Jiminny 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.

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Setup Jiminny with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index Jiminny data for semantic search

Convert your call transcripts into a vector-indexed knowledge base. Use `get_transcript` to pull data and let LlamaIndex handle the embedding process. This makes past customer interactions searchable by meaning rather than just keywords. You find exact moments from previous sales calls instantly.

Ground answers in real-time call data

Stop relying on generic AI responses and use live data from your MCP server. Your RAG application queries `get_call_stats` to provide accurate, evidence-backed answers. This grounds your agent's output in actual performance metrics. You get reliable reports based on what is happening on the phones today.

Filter tools for specific agent tasks

Control exactly which capabilities your agent can access. Use the allowed_tools filter to restrict the agent to `get_user` or `list_teams` when needed. This keeps your agent focused on its specific goal. It prevents the model from attempting irrelevant actions during the index retrieval process.

Setup guide

Set up Jiminny MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
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 provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to 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 Jiminny MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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 Jiminny tools.",
)
response = await agent.run("List recent Jiminny data")

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Common questions about Jiminny MCP in LlamaIndex

Use the McpToolSpec to wrap the server tools and convert them into a list. Once converted, you can feed these tools directly into your FunctionAgent. It creates a seamless link between your API and the index.

Absolutely, it queries the server dynamically via the tools. When a user asks a question, the agent calls `list_activities` to find relevant records. It then pulls the specific transcript needed to answer.

You can trigger a re-index cycle after your agent fetches new data from the server. Since the tools return live results, your agent always has access to the latest call logs. It keeps your knowledge base current.

Yes, you can enable resource access to read data directly from the server. This is useful for building indexes without needing the agent to call a tool for every single item. It makes the data ingestion faster.

Your transcripts are only pulled into your local vector store during the index update. The server manages access via a secure token. Your sensitive sales data stays entirely within your controlled environment.

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