Can I search for a specific player by name?

Yes! Use the `list_players` tool and provide the name in the `search` parameter. Your agent will return a list of matching players with their unique IDs and metadata.

How do I see the scores for all games played on a specific date?

Use the `list_games` tool and provide the date in the `dates` parameter (format YYYY-MM-DD). The response will include all games played on that day with their final scores.

Does the integration provide player season averages?

Absolutely. Use the `get_season_averages` tool by providing the specific season year and the player IDs. This will return averaged performance metrics for the requested period.

BallDontLie MCP Server for LangChain 8 tools — connect in under 2 minutes

Name: BallDontLie
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect BallDontLie through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "balldontlie": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using BallDontLie, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 BallDontLie MCP Server

Equip your AI agent with the most accessible NBA intelligence via BallDontLie. This unified server provides your agent with instant access to extensive basketball data, including player profiles, team technical details, and historical game results. Your agent can search for specific players, audit team rosters, and retrieve real-time or historical statistics without you ever manually checking a sports site. Whether you are analyzing season averages or tracking live game scores, your agent acts as a dedicated sports data analyst through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with BallDontLie through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Player Intelligence — Search for thousands of active and retired NBA players and retrieve their detailed metadata.
Team Auditing — Fetch technical details and identifiers for all 30 NBA teams.
Game Tracking — Retrieve lists of games with scores and specific results, filtered by date or season.
Statistical Analysis — Access player statistics for specific games and calculate season averages for deep performance auditing.
Comprehensive Metadata — List and inspect technical identifiers for teams and players to build complex sports workflows.

The BallDontLie MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain 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 BallDontLie to LangChain via MCP

Follow these steps to integrate the BallDontLie MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from BallDontLie via MCP

Why Use LangChain with the BallDontLie MCP Server

LangChain provides unique advantages when paired with BallDontLie through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine BallDontLie MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across BallDontLie queries for multi-turn workflows

BallDontLie + LangChain Use Cases

Practical scenarios where LangChain combined with the BallDontLie MCP Server delivers measurable value.

RAG with live data: combine BallDontLie tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query BallDontLie, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain BallDontLie tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every BallDontLie tool call, measure latency, and optimize your agent's performance

BallDontLie MCP Tools for LangChain (8)

These 8 tools become available when you connect BallDontLie to LangChain via MCP:

get_game_details

Get game details

get_player_details

Get player details

get_season_averages

Get season averages

get_team_details

Get team details

list_games

List NBA games

list_player_stats

List player statistics

list_players

List or search NBA players

list_teams

List all NBA teams

Example Prompts for BallDontLie in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with BallDontLie immediately.

"What are LeBron James's career stats on BallDontLie?"

"Show me the scores for NBA games yesterday."

"Get the season averages for player ID 237 in 2023."

Troubleshooting BallDontLie MCP Server with LangChain

Common issues when connecting BallDontLie to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

BallDontLie + LangChain FAQ

Common questions about integrating BallDontLie MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.