How to Use the Codemagic MCP in LlamaIndex

Index your mobile pipeline data in LlamaIndex. Turn build logs and artifact details into a queryable knowledge base for your AI.

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Connect Codemagic MCP to LlamaIndex

Create your Vinkius account to connect Codemagic 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 Codemagic with LlamaIndex

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Index Codemagic build data for LlamaIndex

Use the MCP Server to feed build results directly into your vector store. Your agent indexes `get_artifact` data to track release history. This turns your CI/CD metrics into searchable knowledge. You can query past build configurations or artifact locations using natural language.

Ground your agent in real pipeline data

LlamaIndex connects your agent to the live state of your mobile builds. It uses `start_build` to act and then indexes the outcome for future reference. This prevents the agent from hallucinating build statuses. Every answer is backed by the actual API response from your project.

Automate artifact handling with LlamaIndex

Generate public links for your testers using `create_public_artifact_url` and store those results in your index. Your agent retrieves these later. This creates a unified view of your mobile releases. You search for a build version and the agent provides the exact artifact URL stored in your knowledge base.

Setup guide

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

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

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Common questions about Codemagic MCP in LlamaIndex

Use the MCP tool spec for LlamaIndex to wrap the server. This converts all available tools into functions your agent can query and index.

Yes, by indexing the output of your tool calls. You effectively build a RAG application that knows exactly what happened in your last five builds.

Once you run a tool and index the result, that configuration becomes part of your knowledge base. You can ask your agent about current environment variables.

The server requires your API key in the execution environment. The client handles the secure passage of this token to the Codemagic endpoints.

The server only touches artifact metadata and access URLs. Your binary assets remain on the secure storage layer, and the agent only sees what you explicitly index.

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