MCP Servers

A collection of Model Context Protocol servers, templates, tools and more.

MCP server for evidence-based bullet point validation. Scores lists against cognitive research (Miller's Law, serial position effects) with actionable feedback.

Created 12/17/2025
Updated about 12 hours ago
Repository documentation and setup instructions

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

Quick Setup
Installation guide for this server

Install Package (if required)

npx @modelcontextprotocol/server-bullet-mcp

Cursor configuration (mcp.json)

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