How to Use the Chess.com MCP in LlamaIndex

Q: How can I build a RAG app over Chess.com game data using LlamaIndex?

Use the McpToolSpec to load the tools into your agent. Then, create a data pipeline that calls getplayermonthlygames and ingests the PGN data into a VectorStoreIndex. Now you can query it with a QueryEngine.

Q: Can LlamaIndex help me find titled players on Chess.com?

Yes. Call gettitledplayers to get a list of, say, all International Masters. You can then index their profiles using getplayerprofile to create a knowledge base you can query with natural language.

Q: Is it possible to search for Chess.com players from a specific country with LlamaIndex?

Definitely. Use the getcountryplayers tool to get a list of usernames. Then, use LlamaIndex to index their stats and profiles, creating a searchable dataset of players from that country.

Q: How does this MCP Server connect to my LlamaIndex agent?

You just need the Vinkius endpoint URL. The llama-index-tools-mcp library handles the connection and makes the tools available to your agent. No OAuth, no headaches.

Index Chess.com data into a queryable knowledge base for your LlamaIndex RAG apps with this dedicated MCP Server.

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Connect Chess.com MCP to LlamaIndex

Create your Vinkius account to connect Chess.com 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 Chess.com with LlamaIndex

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Build a Player Knowledge Base

Don't just fetch data; index it. Use the Chess.com tools to pull a player's history with `get_player_monthly_games` for every month available from `get_player_game_archives`. LlamaIndex turns this pile of PGNs into a structured, searchable vector index. Now you can ask real questions. 'What were Nakamura's results in blitz games last March?' or 'Show me all games where this player faced the Sicilian Defense.' Your RAG application gives you answers grounded in their actual game data, not a guess.

Create a Searchable Puzzle Library

Build a custom tactics library. Call `get_random_puzzle` or `get_daily_puzzle` repeatedly and use LlamaIndex to index the FEN, solution, and source game metadata for each one. You're creating your own private, queryable database of chess positions. This lets you search semantically. Ask your agent to 'find puzzles that came from games won by Magnus Carlsen' or 'show me tactical positions from the Ruy Lopez opening.' Your agent searches the index you built, not the live web.

Index and Query Chess.com Club Data with LlamaIndex

Point your LlamaIndex agent at a chess community. Use `get_club_members` to get a full roster, then loop through them with `get_player_stats` to get everyone's ratings. Index all of it. You've just built a private intelligence tool for that club. Now you can ask your RAG app: 'Who are the top 10 blitz players in this club?' or 'How many members are FIDE masters?' The answers come directly from the indexed MCP tool output.

Setup guide

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

Use the `McpToolSpec` to load the tools into your agent. Then, create a data pipeline that calls `get_player_monthly_games` and ingests the PGN data into a `VectorStoreIndex`. Now you can query it with a `QueryEngine`.

Yes. Call `get_titled_players` to get a list of, say, all International Masters. You can then index their profiles using `get_player_profile` to create a knowledge base you can query with natural language.

Definitely. Use the `get_country_players` tool to get a list of usernames. Then, use LlamaIndex to index their stats and profiles, creating a searchable dataset of players from that country.

You just need the Vinkius endpoint URL. The `llama-index-tools-mcp` library handles the connection and makes the tools available to your agent. No OAuth, no headaches.

Your agent requests public data like game PGNs, player stats, and puzzle FENs. Vinkius processes these requests in a zero-trust environment, and no data is stored or logged after your session ends.

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