How to Use the DeveloperHub MCP in LlamaIndex

Q: How do I integrate DeveloperHub search with LlamaIndex?

You map the searchdocumentationportal tool to a FunctionAgent. This allows your LlamaIndex application to perform semantic lookups across your documentation repository.

Turn docs into a searchable brain. Use the DeveloperHub MCP Server to build RAG apps with LlamaIndex.

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Connect DeveloperHub MCP to LlamaIndex

Create your Vinkius account to connect DeveloperHub 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 DeveloperHub with LlamaIndex

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Index your documentation with LlamaIndex

Call `get_documentation_sitemap` to crawl your entire portal structure. Your LlamaIndex pipeline treats this map as the primary source for building your knowledge graph. It feeds the output into your vector store for semantic search. You end up with a queryable index that understands the hierarchy of your technical content.

Ground your answers in live data

Use `get_documentation_page_content` to grab full page text for your RAG pipeline. LlamaIndex then chunks this data for accurate retrieval during user queries. This ensures your agent answers questions based on real docs. No hallucinations, just grounded technical facts from your own portal.

Manage project versions with ease

Trigger `list_documentation_versions` to ensure your index points to the right release. It stops your agent from referencing deprecated documentation. Your LlamaIndex agent can switch between versions dynamically. It calls the tool, updates the context, and keeps your search results relevant to the current product version.

Setup guide

Set up DeveloperHub 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 DeveloperHub 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 DeveloperHub tools.",
)
response = await agent.run("List recent DeveloperHub 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 DeveloperHub. 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 DeveloperHub MCP in LlamaIndex

You map the `search_documentation_portal` tool to a FunctionAgent. This allows your LlamaIndex application to perform semantic lookups across your documentation repository.

Only if you configure your vector store that way. The server itself is ephemeral and only serves data when the LlamaIndex client requests it.

Yes. You index the tool outputs into a persistent vector store. This lets your LlamaIndex application reference past documentation states over time.

You provide a single endpoint token when setting up the client. The Vinkius infrastructure handles the rest, keeping your credentials out of your local code.

All communication happens over secure channels. We use memory-isolated sandboxes so your sensitive documentation content never touches disk storage during processing.

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