How to Use the Countly MCP in LlamaIndex

Q: Does Countly work with LlamaIndex filtering?

You can use the allowedtools filter to restrict which metrics the agent can access. This keeps your search index focused and secure.

Index your product metrics and behavioral logs into a LlamaIndex knowledge base.

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Connect Countly MCP to LlamaIndex

Create your Vinkius account to connect Countly 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 Countly with LlamaIndex

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Index Countly metrics with LlamaIndex

Use `read_metrics` to grab session and country data for your knowledge graph. LlamaIndex then turns these API responses into vector embeddings for semantic search. Your agent answers questions about user trends by querying this local index. It stops the agent from guessing and forces it to look at the actual stats.

Search custom events within LlamaIndex

Call `read_events` to retrieve specific user actions and store them as searchable context. You can ask your agent about past user behavior and get answers grounded in real logs. Keep your index fresh by running these tools periodically. The agent always has the latest behavioral data at its fingertips.

Sync user details into LlamaIndex

Update your user records using `update_user_details` and let the index reflect those changes instantly. It ensures your RAG pipeline has the most current user context available. Combine this with `read_drill` to pull specific segments into your knowledge base. You can then perform complex queries across your entire user population.

Setup guide

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

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Common questions about Countly MCP in LlamaIndex

Initialize the client and wrap it in an McpToolSpec. Once you convert the tools to a list, your agent can index the results of any call.

Absolutely. By including the tools in your agent, the output is indexed as part of your document store for later retrieval.

You can use the allowed_tools filter to restrict which metrics the agent can access. This keeps your search index focused and secure.

The data is converted to vectors and stored locally in your index. It remains available for the agent to reference during future queries.

This server only reads the specific event logs you request. We never store raw user PII on the Vinkius platform, keeping your analytics private.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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