How to Use the Cisco Meraki MCP in LlamaIndex

Index your Cisco Meraki network state into a searchable knowledge base using LlamaIndex.

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Connect Cisco Meraki MCP to LlamaIndex

Create your Vinkius account to connect Cisco Meraki 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 Cisco Meraki with LlamaIndex

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Create searchable Cisco Meraki indices

Use `list_network_clients` to pull active connection data and feed it into a LlamaIndex vector store. Your agent can then perform semantic searches across historical network logs. Stop guessing where a device was connected last week. The agent queries the index to find patterns in client behavior that standard dashboards miss.

Ground answers in live Cisco Meraki data

Connect the `get_network_summary` tool to your RAG pipeline to ensure your agent provides answers based on the current state of your infrastructure. Hallucinations disappear when the agent queries the live API. LlamaIndex treats your network configuration as a document source. You get precise, grounded responses about your deployment status whenever you ask a question.

Filter Cisco Meraki data for specific insights

Apply custom filters to `list_organization_networks` to narrow down the knowledge base to specific sites or regions. This keeps your vector store relevant and performant. You avoid loading unnecessary API data into your index. The agent only retrieves what is required to answer the current prompt.

Setup guide

Set up Cisco Meraki 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 Cisco Meraki 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 Cisco Meraki tools.",
)
response = await agent.run("List recent Cisco Meraki data")

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Common questions about Cisco Meraki MCP in LlamaIndex

You define a tool spec that pulls data from the server, which LlamaIndex then vectorizes. This allows for natural language queries against your network inventory.

Absolutely, once the data is indexed, you can run queries against past snapshots. The agent retrieves the relevant records from your vector store.

Your data is indexed locally within your chosen vector store provider. No API secrets are exposed to the embedding model itself.

It is fully compatible. The McpToolSpec converts the API endpoints into standard functions that LlamaIndex executes automatically.

The server only transmits device names, connection timestamps, and network summaries. Sensitive credentials are never indexed or saved.

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