How to Use the ConfigCat MCP in LlamaIndex

Q: How do I index ConfigCat flag data into a LlamaIndex vector store?

Use McpToolSpec with the BasicMCPClient to fetch flag data. You can then pass the output of listsettings directly into your index pipelines.

Q: How does LlamaIndex prevent stale ConfigCat data in its index?

You can set up your pipeline to periodically call listconfigs to refresh the vector store. This ensures your agent always queries active flag configurations.

Q: Can I limit which tools the MCP Server exposes?

Yes, you can use the allowedtools filter when configuring this MCP Server to restrict the agent to read-only tools like getsettingvalue if you want to block write access.

Q: Where does LlamaIndex store my ConfigCat targeting rules?

Targeting rules fetched via getsegment are stored in your local vector index. They never leave your infrastructure, and the Vinkius MCP sandbox keeps your API credentials locked down.

Index your ConfigCat feature flags into LlamaIndex for RAG-driven release management.

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Create your Vinkius account to connect ConfigCat 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 ConfigCat with LlamaIndex

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Index ConfigCat flag data for RAG queries

LlamaIndex does more than run tools; it indexes the outputs. When your agent calls `list_settings`, LlamaIndex stores the current flag configurations in a vector database so you can run semantic searches over your release states. This lets you ask natural language questions about which flags are active without manually parsing JSON payloads. The agent queries the index to find out who targeted a specific segment.

Ground LlamaIndex agent decisions in live flag states

Prevent hallucinations by grounding your agent in real-time data. By calling `get_setting_value` through this MCP Server, your agent knows the exact state of your production environment before drafting release notes. The agent combines live data from `list_environments` with your local documentation. This ensures your release summaries match what is actually deployed in the real world.

Search targeting rules across environments

Your agent can search through complex targeting rules using this MCP Server by combining `list_segments` and `get_segment` into its query index. This allows LlamaIndex to map out which customer segments are seeing which features. Instead of clicking through a dashboard, you ask your agent to analyze your targeting logic. The agent pulls the rules, indexes them, and explains who gets the new UI.

Setup guide

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

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Common questions about ConfigCat MCP in LlamaIndex

Use `McpToolSpec` with the `BasicMCPClient` to fetch flag data. You can then pass the output of `list_settings` directly into your index pipelines.

Yes, the agent can read a feature specification document and call `create_setting` to set up the flag. It maps the requirements in your docs directly to the new flag configuration.

You can set up your pipeline to periodically call `list_configs` to refresh the vector store. This ensures your agent always queries active flag configurations.

Yes, you can use the `allowed_tools` filter when configuring this MCP Server to restrict the agent to read-only tools like `get_setting_value` if you want to block write access.

Targeting rules fetched via `get_segment` are stored in your local vector index. They never leave your infrastructure, and the Vinkius MCP sandbox keeps your API credentials locked down.

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