How to Use the Mod.io MCP in LlamaIndex

Q: How do I index Mod.io content in LlamaIndex?

Fetch the data using getmods and pass the results to your LlamaIndex document loader. The system creates embeddings from the response body for semantic retrieval.

Q: Can LlamaIndex query Mod.io API results directly?

Yes. You wrap the tools in an McpToolSpec and pass them to your agent. It executes the call and indexes the return value automatically.

Q: How do I restrict LlamaIndex to specific Mod.io games?

Use the allowedtools filter in your agent configuration. You only expose the functions necessary for the specific game scope you are indexing.

Index your game library with LlamaIndex. Turn live mod.io data into a queryable knowledge base for your AI.

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Connect Mod.io MCP to LlamaIndex

Create your Vinkius account to connect Mod.io 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 Mod.io with LlamaIndex

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Vectorized Mod Indexing

Convert `get_mods` results into vector embeddings for semantic search. You query your local knowledge base to find mods by description or intent rather than static IDs. LlamaIndex handles the heavy lifting of turning API responses into searchable nodes. It makes your agent smarter about the available content.

Knowledge-Grounded Mod Queries

Use `get_mod_files` to pull technical details into your index. Your agent answers questions about file compatibility by checking the actual data indexed in your RAG pipeline. This removes hallucination risks. The agent relies on the live data retrieved from the server, not its training set.

Unified Mod Search

Combine `get_games` and `get_collections` into a single index. Your agent searches across the entire platform to provide comprehensive answers to user requests. You define the retrieval strategy, and LlamaIndex ensures the Mod.io data is always current.

Setup guide

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

Fetch the data using `get_mods` and pass the results to your LlamaIndex document loader. The system creates embeddings from the response body for semantic retrieval.

Yes. You wrap the tools in an `McpToolSpec` and pass them to your agent. It executes the call and indexes the return value automatically.

LlamaIndex caches the indexed data in your vector store. You control the TTL and refresh frequency for all retrieved API content.

Use the `allowed_tools` filter in your agent configuration. You only expose the functions necessary for the specific game scope you are indexing.

Data is stored in your local vector database. We never persist your OAuth credentials, ensuring your session remains isolated to the current execution.

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