MCP-F1analisys vs memory
Side-by-side comparison to help you pick between these two MCP servers.
MCP-F1analisys by Maxbleu | memory by modelcontextprotocol | |
|---|---|---|
| Stars | ★ 0 | ★ 85,748 |
| 30d uses | — | — |
| Score | 32 | 77 |
| Official | — | ✓ |
| Categories | AI / LLM ToolsOther | Knowledge GraphAI / LLM ToolsProductivity |
| Language | Python | TypeScript |
| Last commit | 6 mo ago | this month |
MCP-F1analisys · Summary
An MCP server providing Formula 1 racing analysis tools through the Model Context Protocol.
memory · Summary
An MCP server implementing persistent memory using a local knowledge graph for AI models to remember user information across chats.
MCP-F1analisys · Use cases
- Analyze F1 race performance metrics and comparisons
- Generate insights about team and driver performance across sessions
- Create visual representations of race position evolution and lap time distribution
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
MCP-F1analisys · Install
Install the package using pip:
pip install mcp-f1analisysConfigure Claude Desktop by adding this to your configuration file:
{
"mcpServers": {
"mcp-f1analisys": {
"command": "python",
"args": [ "-m", "mcp-f1analisys" ]
}
}
}For development testing:
mcp dev .\server.pymemory · 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"]
}
}
}