OpenLigaDB MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OpenLigaDB as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to OpenLigaDB. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in OpenLigaDB?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About OpenLigaDB MCP Server
Empower your AI agent to orchestrate your entire football intelligence workflow with OpenLigaDB, the community-driven platform for sports results. By connecting OpenLigaDB to your agent, you transform complex match tracking into a natural conversation. Your agent can instantly retrieve match results for dozens of leagues, audit current standing tables, and query upcoming fixtures without you ever touching a sports app. Whether you are building a sports blog or monitoring your favorite team, your agent acts as a real-time sports analyst, ensuring your football data is always current and detailed.
LlamaIndex agents combine OpenLigaDB tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Match Auditing — Query full match results for any supported league and season to maintain a clear view of historical performance.
- Table Oversight — Retrieve real-time standing tables to understand league positions and point distributions instantly.
- Fixture Discovery — Query upcoming and most recent matches for any league to maintain strict control over event schedules.
- Match Intelligence — Retrieve detailed metadata for specific match IDs, including scores and goal details.
- League Discovery — List all available leagues in the OpenLigaDB catalog to identify regional event markers.
The OpenLigaDB MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect OpenLigaDB to LlamaIndex via MCP
Follow these steps to integrate the OpenLigaDB MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from OpenLigaDB
Why Use LlamaIndex with the OpenLigaDB MCP Server
LlamaIndex provides unique advantages when paired with OpenLigaDB through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OpenLigaDB tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OpenLigaDB tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OpenLigaDB, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OpenLigaDB tools were called, what data was returned, and how it influenced the final answer
OpenLigaDB + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OpenLigaDB MCP Server delivers measurable value.
Hybrid search: combine OpenLigaDB real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OpenLigaDB to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying OpenLigaDB for fresh data
Analytical workflows: chain OpenLigaDB queries with LlamaIndex's data connectors to build multi-source analytical reports
OpenLigaDB MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect OpenLigaDB to LlamaIndex via MCP:
get_last_league_match
Get information about the most recent match in a league
get_league_matches
Get all matches for a specific league and season
get_league_table
Get the current standing table for a league and season
get_match_details
Get full details for a specific match ID
get_next_league_match
Get information about the next match in a league
list_available_leagues
List all available leagues in the OpenLigaDB catalog
Example Prompts for OpenLigaDB in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OpenLigaDB immediately.
"Show results for Bundesliga 1 (bl1) season 2023 using OpenLigaDB."
"What is the next match in 'bl1'?"
"List all available leagues in OpenLigaDB."
Troubleshooting OpenLigaDB MCP Server with LlamaIndex
Common issues when connecting OpenLigaDB to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpenLigaDB + LlamaIndex FAQ
Common questions about integrating OpenLigaDB MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect OpenLigaDB with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect OpenLigaDB to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.