B
Braffolk/mcp-summarization-functions
Shorten tool output so agents spend fewer tokens
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Client config (built from the install command this repo gives)
{
"mcpServers": {
"braffolk-mcp-summarization-functions": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}Parsed from the README we fetched on 2026-11-06; we have not executed it.
command/args come from the install command the repository states. When it is ambiguous, the project README wins.
VS Code (.vscode/mcp.json - the key is servers)
{
"servers": {
"braffolk-mcp-summarization-functions": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}Same server, but VS Code keeps it under <code>servers</code>.
Claude Code (command line)
claude mcp add --transport stdio braffolk-mcp-summarization-functions -- npx -y @smithery/cli
Claude Code does not read a config file; add the server with this command.
Key names differ by client: Clients & CLI
- Source
- collected from the abordage/awesome-mcp list · type: MCP server
- Harvest history
- first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
- Score
- score 1,003 · 10 sources · mcp
- Install
npx -y @smithery/cli
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
npx -y @smithery/cli. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-11-05；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.
# 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
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