How to Use the Unleash (Feature Toggles) MCP in LlamaIndex

Q: How do I list all available projects using Unleash (Feature Toggles) and LlamaIndex?

You call listprojects. The resulting project names and IDs are immediately indexed into your knowledge base. This means you can query them later just like any document.

Q: If I need to know about feature segments, what tool should I use with Unleash (Feature Toggles) and LlamaIndex?

Use listsegments. The list of segment names is then added to your index. Your agent can retrieve these details later when answering a user query.

Q: Can I find all available environments using Unleash (Feature Toggles) and LlamaIndex?

Yes, use listenvironments. The output is a structured list of all defined environments that you can add to your searchable knowledge index.

Q: If I need to check client metrics data with Unleash (Feature Toggles) and LlamaIndex?

You use reportclientmetrics. The usage data reported here is structured and can be indexed. This allows you to ask complex questions about historical flag utilization.

Turn feature configuration data into knowledge with Unleash (Feature Toggles) and LlamaIndex's RAG capabilities.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Unleash (Feature Toggles) MCP to LlamaIndex

Create your Vinkius account to connect Unleash (Feature Toggles) 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.

GDPR Free for Subscribers

Setup Unleash (Feature Toggles) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Query Feature Flags via `get_client_features`

The `get_client_features` tool fetches all feature flags and strategies for server-side evaluation. LlamaIndex ingests this output, indexing the flag name, strategy, and description into your vector store. Now you can query past sessions about what 'Feature X' meant last week, grounding answers in actual API data, not just general knowledge.

Index Project Configurations

Listing projects is simple with `list_projects`. The output provides a list of all available project IDs. By indexing these results, your RAG application can answer questions like, 'Which projects have billing enabled?' based on the raw data. The MCP Server treats this configuration data as searchable knowledge for future queries.

Understand Environment Context with Unleash (Feature Toggles) and LlamaIndex

Use `list_environments` to pull all configured environments. Indexing these details allows your AI client to answer complex questions like, 'What were the flag rules for staging last quarter?' This keeps historical configuration data searchable alongside documents.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Unleash. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Unleash (Feature Toggles) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Unleash (Feature Toggles) MCP in LlamaIndex

You call `list_projects`. The resulting project names and IDs are immediately indexed into your knowledge base. This means you can query them later just like any document.

Use `list_segments`. The list of segment names is then added to your index. Your agent can retrieve these details later when answering a user query.

This server manages user configuration details, specifically through `list_users`. The resulting list of user IDs and associated properties gets indexed. Your RAG app can then answer questions about specific user groups.

Yes, use `list_environments`. The output is a structured list of all defined environments that you can add to your searchable knowledge index.

You use `report_client_metrics`. The usage data reported here is structured and can be indexed. This allows you to ask complex questions about historical flag utilization.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript