SportsDB MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add SportsDB as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 SportsDB. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in SportsDB?"
)
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 SportsDB MCP Server
Connect SportsDB (sportdb.dev) to your AI agent for comprehensive sports intelligence across hundreds of leagues worldwide.
LlamaIndex agents combine SportsDB tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the 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
- Team & Player Search — Find teams and players by name across all sports and leagues
- Event Search — Search for matches and events by name or keyword
- League Navigation — Browse all sports, leagues, and countries in the database
- Match Tracking — Check upcoming fixtures and recent results for any team or league
- League Tables — Access current standings with points, goals, and form data
- Team & Player Profiles — Detailed info including badges, venues, and career stats
The SportsDB MCP Server exposes 12 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 SportsDB to LlamaIndex via MCP
Follow these steps to integrate the SportsDB 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 12 tools from SportsDB
Why Use LlamaIndex with the SportsDB MCP Server
LlamaIndex provides unique advantages when paired with SportsDB through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine SportsDB tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain SportsDB tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query SportsDB, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what SportsDB tools were called, what data was returned, and how it influenced the final answer
SportsDB + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the SportsDB MCP Server delivers measurable value.
Hybrid search: combine SportsDB real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query SportsDB 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 SportsDB for fresh data
Analytical workflows: chain SportsDB queries with LlamaIndex's data connectors to build multi-source analytical reports
SportsDB MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect SportsDB to LlamaIndex via MCP:
get_last_events
Get last results for a team
get_league_details
Get detailed information about a league
get_league_table
Get league standings/table
get_next_events
Get upcoming events for a team
get_player_details
Get detailed information about a player
get_team_details
Get detailed information about a team
list_all_countries
List all countries with sports data
list_all_leagues
List all available leagues
list_all_sports
List all available sports
search_events
Search for sports events by name
search_players
Returns player metadata including team and nationality. Search for players by name
search_teams
Returns team metadata including ID, sport, league, and country. Search for sports teams by name
Example Prompts for SportsDB in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with SportsDB immediately.
"Show me the current Premier League standings."
Troubleshooting SportsDB MCP Server with LlamaIndex
Common issues when connecting SportsDB to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSportsDB + LlamaIndex FAQ
Common questions about integrating SportsDB 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 SportsDB with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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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 SportsDB to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.