LaunchDarkly MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LaunchDarkly as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to LaunchDarkly. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in LaunchDarkly?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LaunchDarkly MCP Server
Connect your LaunchDarkly platform to any AI agent to monitor experiments and toggle feature flags without breaking your flow.
LlamaIndex agents combine LaunchDarkly tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Flag Management: List existing configurations and inspect deployment flags.
- Environment Variables: Map contexts directly from your active workspaces.
- Experiments: Safely inspect tracking parameters and current user engagement strategies.
The LaunchDarkly MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect LaunchDarkly to LlamaIndex via MCP
Follow these steps to integrate the LaunchDarkly MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from LaunchDarkly
Why Use LlamaIndex with the LaunchDarkly MCP Server
LlamaIndex provides unique advantages when paired with LaunchDarkly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LaunchDarkly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LaunchDarkly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LaunchDarkly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LaunchDarkly tools were called, what data was returned, and how it influenced the final answer
LaunchDarkly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LaunchDarkly MCP Server delivers measurable value.
Hybrid search: combine LaunchDarkly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LaunchDarkly to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying LaunchDarkly for fresh data
Analytical workflows: chain LaunchDarkly queries with LlamaIndex's data connectors to build multi-source analytical reports
LaunchDarkly MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect LaunchDarkly to LlamaIndex via MCP:
get_environment
Get details regarding an environment
get_feature_flag
Get in-depth specifics for a feature flag
get_metric
Get details for a specific metric
get_project
Get details for a specific project
list_audit_logs
Retrieve audit log entries for the account
list_environments
g. Test, Production). Retrieve all environments within a project
list_feature_flags
Retrieve feature flags within a project
list_metrics
Retrieve experimentation metrics within a project
list_projects
Retrieve a list of LaunchDarkly projects
Example Prompts for LaunchDarkly in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LaunchDarkly immediately.
"Check if the newly implemented dark mode feature flag is switched on in Production."
"Turn off the experimental flag targeting our staging environment immediately."
"List all active environments linked to our main workspace project."
Troubleshooting LaunchDarkly MCP Server with LlamaIndex
Common issues when connecting LaunchDarkly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLaunchDarkly + LlamaIndex FAQ
Common questions about integrating LaunchDarkly MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect LaunchDarkly with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LaunchDarkly to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.