B
Braffolk/mcp-summarization-functions
Shorten tool output so agents spend fewer tokens
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置（取自该仓库的安装命令）
{
"mcpServers": {
"braffolk-mcp-summarization-functions": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}解析自我们于 2026-11-06 抓取的该仓库 README；我们未执行过。
command/args 来自仓库写明的安装命令；不确定时请以该项目 README 为准。
VS Code（.vscode/mcp.json —— 键名是 servers）
{
"servers": {
"braffolk-mcp-summarization-functions": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}VS Code 顶层键是 <code>servers</code>，其余与上面那份相同。
Claude Code（命令行）
claude mcp add --transport stdio braffolk-mcp-summarization-functions -- npx -y @smithery/cli
Claude Code 不收 JSON 文件，用这条命令直接加。
配置项的键名以你的客户端为准：客户端与 CLI
- 来源明细
- 收录自 abordage/awesome-mcp 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-11-05 · 最近见到 2026-11-14 · 共见到 10 次 · 被 10 个来源列出
- 评分构成
- 综合分 1,003 · 10 个来源 · mcp
- 安装
npx -y @smithery/cli
常见问题（答案都来自我们的记录）
怎么安装？
该仓库给出的安装命令是
npx -y @smithery/cli。我们的记录里它是仓库自己提供的；不保证在你的客户端上直接可用。它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
我们收录于 2026-11-05；最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
# Summarization Functions
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
### Intelligent text summarization for the Model Context Protocol
Features •
AI Agent Integration •
Installation •
Usage
## Overview
A powerful MCP server that provides intelligent summarization capabilities through a clean, extensible architecture. Built with modern TypeScript and designed for seamless integration with AI workflows.
## Installation
### Installing via Smithery
To install Summarization Functions for Claude Desktop automatically via Smithery:
bash
npx -y @smithery/cli install mcp-summarization-functions --client claude
bash
npm i mcp-summarization-functions
## AI Agent Integration
This MCP server was primarily developed to enhance the performance and reliability of AI agents like Roo Cline and Cline. It addresses a critical challenge in AI agent operations: context window management.
### Context Window Optimization
AI agents frequently encounter situations where their context window gets rapidly filled with large outputs from:
Command execution results
File content readings
Directory listings
API responses
Error messages and stack traces
This server helps maintain efficient context usage by:
1. Providing concise, relevant summaries instead of full content
2. Storing full content for reference when needed
3. Offering focused analysis based on specific needs (security, API surface, etc.)
4. Supporting multiple output formats for optimal context utilization
### Benefits for AI Agents
Reduced Failure Rates: By preventing context window overflow
Improved Response Quality: Through focused, relevant summaries
Enhanced Efficiency: By maintaining important context while reducing noise
Better Resource Management: Through intelligent content caching and retrieval
Flexible Integration: Supporting multiple AI providers and configuration options
### Recommended AI Agent Prompt
When integrating with AI agents, include the following in your agent's instructions:
# CONTEXT MANAGEMENT
You have access to summarization functions through the MCP server. These functions are NOT optional - you MUST use them for ALL potentially large outputs to prevent context overflow:
MANDATORY SUMMARIZATION:
You MUST ALWAYS use summarization functions for:
ANY first time file reading operations (unless you are CERTAIN its small and you are going to edit it)
ALL command execution outputs
EVERY directory analysis
ANY API responses or error logs
ANY output that could be large
NEVER attempt to process raw output directly - ALWAYS use the appropriate summarization function:
• For commands: summarizecommand
• For files: summarizefiles
• For directories: summarizedirectory
• For other text: summarizetext
ALWAYS utilize available features:
• Specify hints for focused analysis
• Choose appropriate output formats
• Use content IDs to access full details only when absolutely necessary
There is NO NEED to process perfect or complete output. Summarized content is ALWAYS preferred over raw data. When in doubt, use summarization.
Summarization in action on the Ollama repository (Gemini 2.0 Flash summarization, Claude 3.5 agent)
## Features
Command Output Summarization
Execute commands and get concise summaries of their output
File Content Analysis
Summarize single or multiple files while maintaining technical accuracy
Features •
AI Agent Integration •
Installation •
Usage
## Overview
A powerful MCP server that provides intelligent summarization capabilities through a clean, extensible architecture. Built with modern TypeScript and designed for seamless integration with AI workflows.
## Installation
### Installing via Smithery
To install Summarization Functions for Claude Desktop automatically via Smithery:
bash
npx -y @smithery/cli install mcp-summarization-functions --client claude
bash
npm i mcp-summarization-functions
## AI Agent Integration
This MCP server was primarily developed to enhance the performance and reliability of AI agents like Roo Cline and Cline. It addresses a critical challenge in AI agent operations: context window management.
### Context Window Optimization
AI agents frequently encounter situations where their context window gets rapidly filled with large outputs from:
Command execution results
File content readings
Directory listings
API responses
Error messages and stack traces
This server helps maintain efficient context usage by:
1. Providing concise, relevant summaries instead of full content
2. Storing full content for reference when needed
3. Offering focused analysis based on specific needs (security, API surface, etc.)
4. Supporting multiple output formats for optimal context utilization
### Benefits for AI Agents
Reduced Failure Rates: By preventing context window overflow
Improved Response Quality: Through focused, relevant summaries
Enhanced Efficiency: By maintaining important context while reducing noise
Better Resource Management: Through intelligent content caching and retrieval
Flexible Integration: Supporting multiple AI providers and configuration options
### Recommended AI Agent Prompt
When integrating with AI agents, include the following in your agent's instructions:
# CONTEXT MANAGEMENT
You have access to summarization functions through the MCP server. These functions are NOT optional - you MUST use them for ALL potentially large outputs to prevent context overflow:
MANDATORY SUMMARIZATION:
You MUST ALWAYS use summarization functions for:
ANY first time file reading operations (unless you are CERTAIN its small and you are going to edit it)
ALL command execution outputs
EVERY directory analysis
ANY API responses or error logs
ANY output that could be large
NEVER attempt to process raw output directly - ALWAYS use the appropriate summarization function:
• For commands: summarizecommand
• For files: summarizefiles
• For directories: summarizedirectory
• For other text: summarizetext
ALWAYS utilize available features:
• Specify hints for focused analysis
• Choose appropriate output formats
• Use content IDs to access full details only when absolutely necessary
There is NO NEED to process perfect or complete output. Summarized content is ALWAYS preferred over raw data. When in doubt, use summarization.
Summarization in action on the Ollama repository (Gemini 2.0 Flash summarization, Claude 3.5 agent)
## Features
Command Output Summarization
Execute commands and get concise summaries of their output
File Content Analysis
Summarize single or multiple files while maintaining technical accuracy
收录徽章（可选）
想把收录标记放进 README 就复制这行，可选，不影响收录。
[](https://anclum.com/zh/s/braffolk-mcp-summarization-functions/)
- 来源
- awesome · https://github.com/Braffolk/mcp-summarization-functions
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