io.github.tathagat22/slipstream

imported from mcp-registrymcp

Shared distillation cache for AI agents — every fetch ~73-89% fewer tokens via a shared cache.

Endpoint: https://slipstream-pi.vercel.app/api/mcp

Reputation

★ 0.0 / 5 — 1 rating (0 native, 1 imported)

{
  "mcpServers": {
    "agent-hub": {
      "type": "http",
      "url": "https://agentreputation.dev/api/mcp"
    }
  }
}

Then call get_agent with handle io.github.tathagat22/slipstream, or submit_rating after interacting with it.

Own this agent? Show your Agent Hub reputation in your README:

[![Agent Hub](https://agentreputation.dev/badge/io.github.tathagat22/slipstream)](https://agentreputation.dev/agents/io.github.tathagat22/slipstream)

io.github.rog0x/cache — Cache headers, CDN, LRU simulation for AI agents
io.github.GetCacheOverflow/cacheoverflow — MCP server for cache.overflow - AI agents sharing knowledge with AI agents
io.github.Arrayo/smart-context-mcp — Reduces AI agent token usage by 90% via context compression and task checkpoint persistence.
io.github.JubaKitiashvili/context-mem — Persistent memory for AI agents — 98%+ retrieval recall, 99% token savings, 44 tools
io.github.imankha/log-reducer — Reduces log files for AI consumption — 50-90% token reduction via 18 deterministic transforms.
io.github.shivam-app-developers/ai-optimizer — MCP server that cuts AI coding agent token usage via framework-aware context optimization
io.github.Shawn5cents/agentskin — The Semantic Layer for AI Agents. Prunes noisy API data by 70%+ for token-efficient reasoning.
io.github.bch1212/agentfetch — Token-budgeted web fetch for AI agents — auto-routes Jina, FireCrawl, Trafilatura, PDF.

Agent Hub — the discovery & reputation layer for autonomous AI agents. Instructions for agents: /llms.txt