claw-tsaver

claw-tsaver

An MCP server that helps AI agents reduce token usage by compressing, summarizing, and managing conversation/context data more efficiently.

Category
访问服务器

README

claw-tsaver

A token-saving MCP proxy for OpenClaw users. Cuts tool call payloads by 90%+ via lazy expansion.

Why

MCP tool calls often return thousands of tokens of HTML or JSON in a single response — but the model typically uses only 5% of it. The remaining 95% silently burns context window and increases cost. claw-tsaver sits between OpenClaw and your downstream MCP servers, intercepts oversized responses, and hands the model a compact preview + an on-demand handle instead.

How

sequenceDiagram
    participant U as OpenClaw (Claude)
    participant C as claw-tsaver proxy
    participant F as fetch / puppeteer / etc.
    U->>C: call_tool("fetch", url)
    C->>F: forward call
    F-->>C: 11,507 tokens of HTML
    Note over C: tiktoken count > threshold
    C->>C: store full content in SQLite
    C-->>U: {preview_head, preview_tail, expand_handle}<br/>(only 104 tokens)
    Note over U: model decides if it needs full text
    U->>C: expand_content(handle)
    C-->>U: full 11,507 tokens

Real measurement

Test Original tokens Returned tokens Saved
fetch Wikipedia "Tokenization (data security)" 11,507 104 99.1%

Tested on OpenClaw + Claude Sonnet 4.6 + mcp-server-fetch, 2026-04-25.
Raw data: benchmarks/mvp-day1-fetch.jsonl.

Quick Start

1. Prerequisites

Install uv (one-time setup):

curl -LsSf https://astral.sh/uv/install.sh | sh

No claw-tsaver install needed — uvx will fetch and run it on demand.

2. Configure downstream MCP servers

Edit ~/.claw-tsaver/config.json (first run of claw-tsaver-mcp will auto-create a template):

{
  "downstream_servers": [
    {"name": "fetch", "command": "uvx", "args": ["mcp-server-fetch"]}
  ],
  "compression_threshold_tokens": 500
}

3. Register with OpenClaw

Add this block at the top level of ~/.openclaw/openclaw.json:

"mcp": {
  "servers": {
    "claw-tsaver": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/Yang1Bai/claw-tsaver",
               "claw-tsaver-mcp"]
    }
  }
}

Then restart OpenClaw gateway: openclaw gateway restart

Dashboard

Optional: a local web UI for real-time token savings stats.

uvx --from git+https://github.com/Yang1Bai/claw-tsaver claw-tsaver-dashboard

Open http://localhost:7878 in your browser.

Roadmap

  • [x] Module A: lazy expansion proxy (this release)
  • [x] Module D: local dashboard (this release)
  • [ ] Module B: tool routing (auto-load only relevant MCPs per turn)
  • [ ] Module C: conversation history compression (atomic fact cards)

License

MIT — see LICENSE file.

Contributing

Issues and PRs welcome.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选