Context GC for LLM agents: offload large tool outputs and recall them to save tokens.
Context GC for LLM agents: offload large tool outputs and recall them to save tokens. That is what the lethe mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
LETHE ships as an MCP server. Two lines and your agent can move big outputs out of its context and recall them on demand — fewer tokens on every long task. / LETHE viene como servidor MCP. Dos líneas y tu agente saca outputs grandes del contexto y los recupera cuando los necesita — menos tokens en cada tarea larga.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
The server publishes 1 tool. What each one is for:
Licencia — Liberado al dominio público bajo la Unlicense. Libre para todos, en cualquier lugarConfiguration is passed through the environment: ANTHROPIC_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Lethe's toolset — Licencia — is a fair guide to whether it matches your workflow. It is maintained by JesusGarcia9009; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Licencia | Liberado al dominio público bajo la [Unlicense](LICENSE). Libre para todos, en cualquier lugar. |
| Variable | Description | Required |
|---|---|---|
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.