BallDontLie MCP. Analyze NBA player stats and game results instantly.
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BallDontLie is an NBA data platform that gives your AI client instant access to extensive basketball intelligence. Use it to search for player profiles, audit team rosters, and pull historical or real-time game results.
It lets your agent act as a dedicated sports data analyst directly within your chat environment, eliminating the need to manually check sports websites for player stats, team details, or game scores.
What your AI agents can do
Get game details
Fetches scores and specific metrics for a single NBA game.
Get player details
Retrieves the full profile and metadata for one NBA player.
Get season averages
Calculates and returns the average points, rebounds, and assists for a player's season.
Retrieves specific scores and statistics for a single NBA game.
Fetches the full profile and metadata for a specific NBA player.
Calculates and returns aggregated season performance metrics for a specified player.
Retrieves technical information and identifiers for a specific NBA team.
Generates a list of NBA games, allowing filtering by date or season.
Retrieves raw player performance data, often useful for calculating custom metrics.
Searches the database and returns a list of NBA players by name or criteria.
Retrieves a complete list of all teams currently in the NBA.
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BallDontLie MCP Server: 8 Tools for NBA Data
These tools let your agent pull everything from player profiles to season averages, making it a complete data source for sports analysis.
019d841bget game details
Fetches scores and specific metrics for a single NBA game.
019d841bget player details
Retrieves the full profile and metadata for one NBA player.
019d841bget season averages
Calculates and returns the average points, rebounds, and assists for a player's season.
019d841bget team details
Gets the roster and technical information for a specific NBA team.
019d841blist games
Provides a list of NBA games, letting you filter by date range or season.
019d841blist player stats
Lists raw, game-by-game player statistics for deep analysis.
019d841blist players
Searches and returns a list of NBA players based on criteria.
019d841blist teams
Lists all 30 NBA teams and their identifiers.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
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- Built in DLP, auth, and compliance on every call
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Make Your AI Do More
Start with BallDontLie, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
Your AI client gets instant access to deep NBA data with BallDontLie. You can use it to search for player profiles, audit team rosters, and pull historical or real-time game results. It lets your agent act like a dedicated sports data analyst right in your chat, so you never gotta check a sports website manually.
You'll find all the info you need to track players and teams.
Player Info
get_player_details pulls the full profile and metadata for any NBA player. You can run list_players to search the database and get a list of players by name or other criteria. If you need to know what a player's season looks like, get_season_averages calculates the average points, rebounds, and assists for a whole season.
You can also pull raw, game-by-game player stats using list_player_stats for deep analysis.
Team and Game Data
list_teams gives you a complete list of all 30 NBA teams and their identifiers. You can get a team's roster and technical info with get_team_details. For games, you can use list_games to generate a list of NBA games, letting you filter by date or season. get_game_details retrieves specific scores and metrics for a single NBA game.
How BallDontLie MCP Works
- 1 Subscribe to the BallDontLie server and provide your API Key.
- 2 Instruct your AI client to perform the desired query (e.g., 'What were the season averages for player 237?').
- 3 The agent calls the appropriate tool (e.g.,
get_season_averages) and returns the structured data directly to your chat window.
The bottom line is that you ask your agent a question, and the server runs the code to give you the answer.
Who Is BallDontLie MCP For?
Sports Analysts who need to audit player performance metrics fast. Fantasy Basketball Players who need to compare roster stats against historical data. Data Scientists building models on sports data. Content Creators needing historical game results for media production.
Uses list_player_stats and get_season_averages to build player performance models and track career trends.
Runs checks using get_player_details and get_game_details to compare current roster performance against historical opponent matchups.
Uses list_games and list_players to pull verifiable historical data for articles or commentary.
Integrates the raw data via list_teams and list_players to populate sports-centric applications.
What Changes When You Connect
- Get a full player profile instantly. Instead of navigating multiple tabs on a sports site, use
get_player_detailsto pull a player's complete metadata right into your chat. - Track season performance over time. Running
get_season_averagesgives you aggregated metrics—points, rebounds, assists—without manually calculating them across dozens of games. - Audit team rosters quickly. Use
get_team_detailsandlist_teamsto get identifiers and technical info for any of the 30 NBA teams in one go. - Check historical game scores. Need to know who beat who last year?
list_gameslets you filter results by date or season to pull specific scores. - Build complex analysis workflows. The tools allow you to chain calls—get player info with
get_player_details, then check their game stats usinglist_player_stats. - Streamline data sourcing. You never have to copy-paste data from a website. Your agent pulls clean, structured JSON directly from the server.
Real-World Use Cases
Checking a player's career trajectory
A sports analyst needs to compare Player X's performance in 2020 vs. 2023. They ask their agent to pull the get_season_averages for both years, then use get_player_details to confirm the player ID. The agent runs both tools, giving the analyst a side-by-side comparison of key metrics.
Verifying a game's outcome
A journalist needs the exact score for a specific game last Saturday. They ask the agent to run list_games filtered by date. The agent returns a list of games, and the journalist then uses get_game_details on the specific game ID to confirm the final score and scorers.
Comparing multiple players' stats
A fantasy manager wants to see how three specific players stacked up in the same season. They first use list_players to get the IDs, then run list_player_stats for each ID. The agent collects all the raw data points, allowing the manager to manually compare performance.
Developing a sports app data layer
A developer needs a reliable data source for team metadata. They run list_teams to get all identifiers, then use get_team_details to pull the necessary technical info for every team they plan to feature in their application.
The Tradeoffs
Querying data in chunks
Manually running list_players to find IDs, then running get_player_details for each ID, and finally running get_season_averages for each ID. This is slow and tedious.
→ Instead, instruct your agent to run a single query: 'What were the season averages for Player X?' The agent handles the sequence of calls internally, minimizing redundant steps.
Assuming data consistency
Using get_game_details to pull scores, but failing to check if the season is active. The data might be incomplete or mismatched.
→
Always start by using list_games to validate the date range and ensure the game exists in the records before calling get_game_details.
Relying on memory
Remembering which specific player ID or team ID you used last time to avoid re-running the query.
→
Use the dedicated tools like list_players or list_teams first. These tools give you the authoritative IDs needed for every subsequent call, preventing errors.
When It Fits, When It Doesn't
Use BallDontLie if your analysis requires concrete, historical, or current NBA game data. You need to know specific player stats, team rosters, or game scores. For example, if you need to see a player's total rebounds for the 2022 season, use get_season_averages. If you just need a list of every player's name, use list_players. Don't use this if you're just trying to guess stats or analyze abstract concepts. If your data needs are non-quantifiable (e.g., 'Is the team's culture positive?'), this server won't help. It only deals in verifiable numbers. If you need to know the roster for every team, run list_teams and then loop through get_team_details for each one.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BallDontLie. 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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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually checking NBA stats is a huge time sink.
Today, checking stats means opening ESPN, clicking on the season tab, searching for the player, finding the specific game, and then manually copy-pasting the stats into a spreadsheet. You spend five minutes just getting the raw numbers.
With BallDontLie, your agent handles the whole process. You just ask: 'What were LeBron James's season averages in 2023?' Your agent calls the tools and gives you a clean, structured data block instantly. No clicking, no copy-pasting, just the answer.
BallDontLie MCP Server: Get Player Intelligence
Before, you had to search multiple sites for player IDs, then use one site for game scores, and another for season averages. It was a mess of tabs and redirects.
Now, your agent uses `get_player_details` to find the player's core info, then uses `get_season_averages` to pull the numbers. The whole thing is managed in a single conversation flow. It just works.
Common Questions About BallDontLie MCP
How do I find a player's stats using list_player_stats? +
The list_player_stats tool returns raw player performance data. You need to specify the player ID and the desired time frame to get the data you need for your analysis.
What is the best way to check scores for a specific game using get_game_details? +
To use get_game_details, you must provide the unique game ID. The tool then pulls the complete score, including quarter-by-quarter metrics and final results.
Can I get all team information at once using list_teams? +
Yes, list_teams gives you a list of all 30 NBA teams and their technical identifiers. You can then use get_team_details with those IDs to get the specific roster information.
How do I find a player's season averages with get_season_averages? +
You provide the player ID and the season year. The tool calculates and returns the average points, rebounds, and assists for that specified season.
What if I need data from a different season? list_games? +
Use list_games first. It lets you filter games by date or season, giving you the list of game IDs you need before calling get_game_details.
How do I list or search for specific NBA players using list_players? +
You call list_players() with search criteria. This tool searches the entire database for players by name, team, or ID. You get a list of profiles and IDs, which you can then use with other tools for deeper analysis.
What's the process for analyzing team rosters using get_team_details? +
Use get_team_details() to fetch technical identifiers and comprehensive data for a specific team. This output includes the full roster list and technical identifiers needed to cross-reference player stats or historical game records.
Does `list_games` allow filtering by date range? +
Yes, list_games accepts date parameters. You can filter games by start date or end date to narrow your search. This lets you retrieve all scores and results for a specific season or time window.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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