mlb-mcp vs memory
Side-by-side comparison to help you pick between these two MCP servers.
mlb-mcp by etweisberg | memory by modelcontextprotocol | |
|---|---|---|
| Stars | ★ 25 | ★ 85,748 |
| 30d uses | — | — |
| Score | 44 | 77 |
| Official | — | ✓ |
| Categories | OtherAI / LLM Tools | Knowledge GraphAI / LLM ToolsProductivity |
| Language | Python | TypeScript |
| Last commit | 2 mo ago | this month |
mlb-mcp · Summary
Python MCP server for MLB baseball analytics with API access to statcast, fangraphs, and baseball reference data.
memory · Summary
An MCP server implementing persistent memory using a local knowledge graph for AI models to remember user information across chats.
mlb-mcp · Use cases
- Baseball analysts can retrieve player statistics and game performance data using MCP tools
- Sports journalists can generate baseball visualizations and statistics reports programmatically
- Fantasy baseball enthusiasts can access up-to-date player data for roster decisions
memory · Use cases
- Personalizing AI assistant interactions by remembering user preferences, history, and relationships
- Building context-aware chat applications that maintain conversation history
- Creating knowledge bases that persist across AI model sessions
mlb-mcp · Install
Installation
- Install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | sh- Create and activate a virtual environment:
uv venv
source .venv/bin/activate # On Unix/macOS
# or
.venv\Scripts\activate # On Windows- Install dependencies:
uv pip install -e .Claude Desktop Integration
To connect this MCP server to Claude Desktop, add a configuration to your claude_desktop_config.json file:
"mcp-baseball-stats": {
"command": "{PATH_TO_UV}",
"args": [
"--directory",
"{PROJECT_DIRECTORY}",
"run",
"python",
"-m",
"mlb_stats_mcp.server"
],
"env": {
"MLB_STATS_LOG_FILE": "{LOG_FILE_PATH}",
"MLB_STATS_LOG_LEVEL": "DEBUG"
}
}memory · Install
Installation
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}VS Code
Use one-click installation buttons or manually configure in .vscode/mcp.json:
{
"servers": {
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}Docker
{
"mcpServers": {
"memory": {
"command": "docker",
"args": ["run", "-i", "-v", "claude-memory:/app/dist", "--rm", "mcp/memory"]
}
}
}