DevCycle MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DevCycle as an MCP tool provider through 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 DevCycle. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in DevCycle?"
)
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 DevCycle MCP Server
Integrate DevCycle, the modern feature flag and experimentation platform, directly into your AI workflow. Manage your feature flags across projects, monitor staging and production environments, and audit targeting rules and variations using natural language.
LlamaIndex agents combine DevCycle tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Feature Flag Management — List, search, and retrieve detailed configuration for all your feature flags.
- Environment Oversight — Monitor project environments, retrieve SDK keys, and track deployment statuses.
- Variable & Variation Tracking — List all defined variables and their variations to ensure technical consistency.
- Operational Control — Update feature flag statuses (active/archived) directly via chat.
The DevCycle MCP Server exposes 10 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 DevCycle to LlamaIndex via MCP
Follow these steps to integrate the DevCycle 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 10 tools from DevCycle
Why Use LlamaIndex with the DevCycle MCP Server
LlamaIndex provides unique advantages when paired with DevCycle through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DevCycle tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DevCycle tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DevCycle, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DevCycle tools were called, what data was returned, and how it influenced the final answer
DevCycle + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DevCycle MCP Server delivers measurable value.
Hybrid search: combine DevCycle real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DevCycle 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 DevCycle for fresh data
Analytical workflows: chain DevCycle queries with LlamaIndex's data connectors to build multi-source analytical reports
DevCycle MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect DevCycle to LlamaIndex via MCP:
get_environment_sdk_keys
List SDK keys for all environments in a project
get_feature_flag_details
Get full configuration and targeting rules for a specific feature flag
get_project_details
Get detailed information for a specific DevCycle project
list_active_flags
Identify feature flags that are currently active
list_devcycle_projects
List all projects in your DevCycle account
list_feature_flags
g. release, ops), and current statuses. List all feature flags within a specific project
list_feature_variables
List all variables defined in a project
list_project_environments
List all environments (e.g. Production, Staging) for a project
search_feature_flags
Search for feature flags in a project by keyword
update_feature_flag_status
Update the status (e.g. active, archived) of a feature flag
Example Prompts for DevCycle in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DevCycle immediately.
"List all feature flags in the project 'Main-App'."
"Show me the configuration for the 'Beta-Feature' flag."
"What are the SDK keys for our 'Production' environment?"
Troubleshooting DevCycle MCP Server with LlamaIndex
Common issues when connecting DevCycle to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDevCycle + LlamaIndex FAQ
Common questions about integrating DevCycle 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 DevCycle 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 DevCycle to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.