How to Use the Heroku (PaaS) MCP in LlamaIndex

Index your Heroku (PaaS) infrastructure data into LlamaIndex for intelligent, queryable documentation.

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Connect Heroku (PaaS) MCP to LlamaIndex

Create your Vinkius account to connect Heroku (PaaS) 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 Heroku (PaaS) with LlamaIndex

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Index Heroku (PaaS) app states

Run `get_app_info` and store the results in your vector index. You can now ask questions about your regional latency and slug sizes across your entire fleet. It turns raw API output into a searchable knowledge base. Your agent retrieves these details to provide grounded answers about your current setup.

Trace infrastructure via LlamaIndex

Use `list_dynos` to capture the current status of your containers. Indexing this data allows you to query the history of your application health over time. Search your index to find patterns in crashes or idle states. It gives you a clear view of how your infrastructure behaves without digging through raw logs.

Manage add-ons with semantic search

Collect data from `list_addons` and integrate it into your RAG pipeline. You can query which apps use specific third-party services to manage your dependencies. This makes it easy to find where specific add-ons are active. Your agent uses this index to verify your configuration across multiple projects.

Setup guide

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

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Common questions about Heroku (PaaS) MCP in LlamaIndex

Once you index the output of the tools, you can run natural language queries against your infrastructure data. It links your API results to your vector store.

The MCP server provides the live data needed to populate your index. You fetch the state, store it, and query it to inform your agent's reasoning.

You control what goes into your index. Avoid indexing sensitive tokens or passwords retrieved from your environment variables to maintain strict data privacy.

Use the MCP tool spec to convert the server tools into a format LlamaIndex understands. The client handles the connection and provides the tools to your agent.

The server provides access to apps, dyno status, and config variables. You choose which of these items to index based on your search requirements.

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