bullet-mcp

bullet-mcp

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

Category
Visit Server

README

bullet-mcp.jpg


bullet-mcp

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

Features

  • Score bullet lists (0-100) against 7 evidence-based rules
  • Letter grades (A/B/C/D/F) with actionable feedback
  • Research citations for each validation rule
  • Context awareness (document, presentation, reference)
  • Sections support for long documents with multiple chapters/topics

Installation

npm install bullet-mcp

Or install globally:

npm install -g bullet-mcp

Usage

Claude Desktop Configuration

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "bullet": {
      "command": "npx",
      "args": ["bullet-mcp"]
    }
  }
}

Tool: bullet

Validates bullet point lists against evidence-based cognitive research.

Input:

{
  "items": [
    { "text": "Use 3-7 items per list for optimal recall", "importance": "high" },
    { "text": "Place critical information first and last" },
    { "text": "Maintain parallel grammatical structure" },
    { "text": "Keep lines between 45-75 characters" },
    { "text": "Limit hierarchy to 2 levels maximum" }
  ],
  "context": "document"
}

Output:

{
  "overall_score": 97,
  "grade": "A",
  "summary": "Excellent bullet list following evidence-based best practices.",
  "top_improvements": ["Consider adding detail or combining with a related point"],
  "errors": [],
  "warnings": [],
  "suggestions": [...]
}

Sectioned Mode (for long documents)

For long documents with multiple chapters or topics, use the sections format. Each section is validated separately (3-7 items per section), allowing unlimited total content.

Input:

{
  "sections": [
    {
      "title": "Chapter 1: Introduction",
      "items": [
        { "text": "Define the problem scope and context" },
        { "text": "Outline key objectives and goals" },
        { "text": "Summarize the main approach taken" }
      ]
    },
    {
      "title": "Chapter 2: Methods",
      "items": [
        { "text": "Describe data collection procedures" },
        { "text": "Explain analysis methodology used" },
        { "text": "Detail validation steps performed" }
      ],
      "context": "reference"
    }
  ],
  "context": "document"
}

Output includes per-section breakdown:

{
  "overall_score": 95,
  "grade": "A",
  "section_scores": [
    { "title": "Chapter 1: Introduction", "score": 96, "grade": "A", "item_count": 3 },
    { "title": "Chapter 2: Methods", "score": 94, "grade": "A", "item_count": 3 }
  ],
  "summary": "Excellent structured summary across 2 sections."
}

Validation Rules

Rule Threshold Research Basis
List Length 3-7 items (5 optimal) Miller (1956), Cowan (2001): Working memory 3-4 chunks
Hierarchy Max 2 levels Kiger (1984), Nielsen: 2-level structures fastest
Line Length 45-75 chars (66 optimal) Typography research on readability
Serial Position Important info first/last Ebbinghaus (1885): U-shaped retention curve
Parallel Structure Consistent grammar Frazier et al. (1984): Faster scanning
First Words Unique, scannable Nielsen eye-tracking: First 2 words critical
Formatting Consistent punctuation Usability research

Context Options

  • document (default): Optimizes for scanning and reference
  • presentation: Warns that visuals may be 43% more persuasive
  • reference: Optimizes for quick lookup

Environment Variables

Variable Default Description
BULLET_STRICT_MODE false Treat warnings as errors
BULLET_NO_CITATIONS false Disable research citations in output
BULLET_NO_COLOR false Disable colored console output

Development

# Install dependencies
npm install

# Build
npm run build

# Test with MCP Inspector
npm run dev

Research Foundation

This tool is based on docs/bullet-study.md, a synthesis of cognitive psychology research on optimal list design including:

  • Working memory capacity (Miller, Cowan)
  • Serial position effects (Ebbinghaus, Murdock)
  • Eye-tracking studies (Nielsen Norman Group)
  • Information architecture (Kiger, Zaphiris)
  • Typography research (45-75 character optimal line length)

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured