cache-proxy
LLM caching proxy (x402 USDC on Base) - exact + semantic cache. Free health.
Should I use this
Quality & Safety
A
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
~240Tokens (tool definitions)
~1.1 KBTypical response size
Minimal attention impact (0.19% of 128k context)
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"cache-proxy": {
"url": "https://cache.api.ainode.tech/mcp"
}
}
}Remote endpoints
https://cache.api.ainode.tech/mcpstreamable-httpWhat it can do
Tool inventory
Tools (2)
🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
⚪health(echo)
Health check. Returns server status and optional echo.
Input Schema
{
"type": "object",
"properties": {
"echo": {
"type": "string",
"description": "Optional string to echo back"
}
},
"additionalProperties": false,
"$schema": "http://json-schema.org/draft-07/schema#"
}🟡cache_query(provider, path, body, api_key, cache_ttl)
Send an LLM request through the caching proxy. Returns cached response if available, otherwise proxies to upstream LLM.
Input Schema
{
"type": "object",
"properties": {
"provider": {
"type": "string",
"enum": [
"openai",
"anthropic"
],
"description": "LLM provider to proxy to"
},
"path": {
"type": "string",
"description": "API path (e.g. /v1/chat/completions)"
},
"body": {
"type": "string",
"description": "JSON request body as string"
},
"api_key": {
"type": "string",
"description": "API key for the upstream provider"
},
"cache_ttl": {
"type": "number",
"description": "Cache TTL in seconds (default 86400)"
}
},
"required": [
"provider",
"path",
"body",
"api_key"
],
"additionalProperties": false,
"$schema": "http://json-schema.org/draft-07/schema#"
}Community
Evidence
Recent observations
verifiedversion not recorded2 tools
verifiedversion not recorded2 tools