bullet-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@bullet-mcp Check my bullet points for the project summary"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.

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
Related MCP server: adaptive-cards-mcp
Installation
npm install bullet-mcpOr install globally:
npm install -g bullet-mcpUsage
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 referencepresentation: Warns that visuals may be 43% more persuasivereference: Optimizes for quick lookup
Environment Variables
Variable | Default | Description |
|
| Treat warnings as errors |
|
| Disable research citations in output |
|
| Disable colored console output |
Development
# Install dependencies
npm install
# Build
npm run build
# Test with MCP Inspector
npm run devResearch 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
Available Tools
1 toolbulletA
Validate and improve bullet point lists using evidence-based cognitive research.
This tool analyzes bullet lists against scientifically-validated principles for optimal recall, scanning efficiency, and comprehension. Use it to ensure your summaries follow best practices.
INPUT MODES:
Flat mode: Use "items" for simple lists (3-7 items recommended)
Sectioned mode: Use "sections" for long documents with multiple topics/chapters
Each section has its own title and items array
The 3-7 item rule applies PER SECTION, allowing unlimited total content
WHEN TO USE:
Before finalizing any bullet list summary
When creating documentation, reports, or reference materials
To score existing bullet content against research standards
For guidance on improving list structure
KEY PRINCIPLES ENFORCED:
List Length (3-7 items per section, 5 optimal): Working memory limits mean more items decrease recall
Hierarchy (max 2 levels): Breadth over depth for better comprehension
Serial Position: Place critical info first and last (U-shaped recall curve)
Line Length (45-75 chars, 66 optimal): Typography research on readability
Parallel Structure: Consistent grammar enables faster scanning
First Two Words: Critical for reader fixation and scanning decisions
CONTEXT AWARENESS:
document: Optimizes for scanning and reference (default)
presentation: Warns that visuals may be more effective (43% more persuasive per research)
reference: Optimizes for quick lookup
Per-section context override supported in sectioned mode
SCORING:
0-100 scale with letter grades (A/B/C/D/F)
Per-rule breakdown with research citations
Per-section breakdown in sectioned mode
Actionable improvement suggestions ranked by impact
Returns JSON with score, grade, issues, and top improvements.
Inputs: title (required), description (required), intro (required), items, +2 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined and distinct.
With a single tool, naming consistency is not applicable; there is no pattern to violate.
The single tool is appropriately scoped for the server's narrow purpose of validating and improving bullet lists, covering all necessary functionality.
The tool comprehensively covers the domain of bullet list validation, including multiple input modes, context awareness, scoring, and improvement suggestions, with no obvious gaps.
Maintenance
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