Zero-dependency, fully-reversible context compression for AI agents — cut LLM token usage up to 94% (JSON tabularization, log template mining, query-aware code skeletons). Stdlib-only, air-gap ready, built-in MCP server.
Setup
Choose your client, copy the config, then fill in any required environment variables.
claude mcp add token-optimizer -- uvx slimctx~/.codex/config.toml
[mcp_servers.token-optimizer]
command = "uvx"
args = ["slimctx"]
# codex mcp add token-optimizer -- uvx slimctx~/.cursor/mcp.json
{
"mcpServers": {
"token-optimizer": {
"command": "uvx",
"args": [
"slimctx"
]
}
}
}.vscode/mcp.json
{
"servers": {
"token-optimizer": {
"type": "stdio",
"command": "uvx",
"args": [
"slimctx"
]
}
}
}~/.gemini/settings.json
{
"mcpServers": {
"token-optimizer": {
"command": "uvx",
"args": [
"slimctx"
]
}
}
}claude_desktop_config.json
{
"mcpServers": {
"token-optimizer": {
"command": "uvx",
"args": [
"slimctx"
]
}
}
}.trae/mcp.json
{
"mcpServers": {
"token-optimizer": {
"command": "uvx",
"args": [
"slimctx"
]
}
}
}