devtu-optimize-descriptions▌
mims-harvard/tooluniverse · updated Apr 8, 2026
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Optimize tool descriptions in ToolUniverse JSON configuration files to ensure they are clear, complete, and user-friendly.
ToolUniverse Tool Description Optimization
Optimize tool descriptions in ToolUniverse JSON configuration files to ensure they are clear, complete, and user-friendly.
When to Apply This Skill
Use when:
- Reviewing newly created tool descriptions
- User asks "are these tools easy to understand?"
- Improving existing tool documentation
- Adding new tools to ToolUniverse
- User mentions tool usability, clarity, or documentation
Quick Optimization Checklist
Tool Description Review:
- [ ] Prerequisites stated (packages, API keys, accounts)
- [ ] Critical abbreviations expanded on first use
- [ ] Required vs optional parameters clear
- [ ] Mutually exclusive options numbered/labeled
- [ ] Parameter guidance includes trade-offs
- [ ] Filter syntax shows available fields
- [ ] File size warnings where relevant
- [ ] Examples show realistic usage
Critical Improvements (Fix Immediately)
1. Clarify Required Input Requirements
Problem: Users don't know if they need ONE input or ALL inputs.
Fix: Use "Required: Provide ONE input type" for mutually exclusive options.
// Before
"description": "Process BED regions, motifs, or gene lists..."
// After
"description": "Process genomic data. **Required: Provide ONE input type** - (1) BED regions, (2) DNA motif, or (3) gene list. Analyzes..."
Number the options and use bold for "Required".
2. Add Prerequisites to First Tool
Problem: Users don't know what to install/configure before use.
Fix: Add prerequisites note to first tool in each family.
"description": "Query single-cell data. Prerequisites: Requires 'package-name' (install: pip install tooluniverse[extra]). Returns..."
Include:
- Package installation command
- API key requirements
- Account creation instructions
3. Expand Critical Abbreviations
Problem: New users don't understand technical terms.
Fix: Expand on first use with format: "Abbreviation (Full Name)".
Common abbreviations to expand:
- H5AD → HDF5-based AnnData
- RPM → Reads Per Million
- TSS → Transcription Start Site
- TAD → Topologically Associating Domain
- DRS → Data Repository Service
- API names (MACS2, IUPAC, etc.)
// Before
"description": "Download H5AD files..."
// After
"description": "Download H5AD (HDF5-based AnnData) files..."
High-Priority Improvements
4. Enhance Filter Parameter Descriptions
Problem: Users don't know what fields are available or what syntax to use.
Fix: List operators, common fields, and provide multiple examples.
"parameter_name": {
"type": "string",
"description": "Filter using SQL-like syntax. Format: 'field == \"value\"'. Operators: ==, !=, in, <, >, <=, >=. Combine with 'and'/'or'. Common fields: tissue, cell_type, disease, assay, sex, ethnicity. Examples: 'tissue == \"lung\"', 'disease == \"COVID-19\" and tissue == \"lung\"', 'cell_type in [\"T cell\", \"B cell\"]'."
}
Include:
- Syntax format
- Available operators
- List of 5-10 common fields
- 2-3 diverse examples
5. Improve Parameter Guidance
Problem: Users don't know which value to choose or what trade-offs exist.
Fix: Explain what each value means and provide recommendations.
// Before
"threshold": "Q-value threshold (05=1e-5, 10=1e-10, 20=1e-20)"
// After
"threshold": "Peak calling stringency. '05'=1e-5 (permissive, more peaks, broad features), '10'=1e-10 (moderate, balanced), '20'=1e-20 (strict, high confidence, narrow peaks). Default '05' suitable for most analyses. Higher values = fewer but more confident peaks."
For each parameter option, explain:
- What it means practically
- When to use it
- Trade-offs involved
- Recommended default
6. Number Mutually Exclusive Options
Problem: Users provide multiple options when only one is allowed.
Fix: Label options as "Option 1", "Option 2", etc.
"bed_data": {
"description": "**Option 1**: BED format regions (tab-separated: chr, start, end). Example: 'chr1\\t1000\\t2000'."
},
"motif": {
"description": "**Option 2**: DNA sequence motif in IUPAC notation. Use: A/T/G/C, W=A|T, S=G|C. Example: 'CANNTG'."
},
"gene_list": {
"description": "**Option 3**: Gene symbols as array. Example: ['TP53', 'MDM2']."
}
Medium-Priority Improvements
7. Add File Size Warnings
For tools that download or return large files:
"description": "Download contact matrices. Note: Files can be large (GBs), check file_size in metadata before downloading. Returns..."
8. Clarify Web Form vs API Results
When tool returns submission URL instead of direct results:
"description": "Perform enrichment analysis. Note: Returns submission URL (web form-based analysis). Analyzes..."
9. Explain File Type Differences
For tools with multiple format options:
"file_type": "File format. Common types: 'cooler' (multi-resolution contact matrices), 'pairs' (aligned read pairs), 'hic' (Juicer format), 'mcool' (multi-resolution cooler)."
Description Structure Template
{
"name": "Tool_operation_name",
"type": "ToolClassName",
"description": "[Action verb] to [purpose]. [Prerequisites if first tool]. [Key data/features]. [Required inputs if mutually exclusive]. [Note about limitations/requirements]. Use for: [use case 1], [use case 2], [use case 3].",
"parameter": {
"properties": {
"param_name": {
"type": "string",
"description": "[What it does]. [Format/syntax if applicable]. [Options with trade-offs]. [Examples]. [Recommendation if applicable]."
}
}
}
}
Description Quality Checklist
Clarity Checks
- Purpose clear in first sentence
- Technical terms expanded
- Prerequisites stated upfront
- Examples show realistic usage
- "Use for:" section lists 3-5 concrete use cases
Completeness Checks
- Required inputs clearly marked
- Parameter choices explained
- Limitations noted (file size, web form, etc.)
- Available fields listed for filters
- Default values recommended
Usability Checks
- New users can understand without external docs
- Users know what to provide
- Users can make informed parameter choices
- Error prevention (mutually exclusive options labeled)
Testing Description Quality
To verify description quality, ask:
-
Can a new user understand what the tool does?
- Read only the description (no docs)
- Should be clear within 30 seconds
-
Can a user provide correct inputs on first try?
- Required inputs obvious
- Format/syntax clear
- Mutually exclusive options labeled
-
Can a user choose appropriate parameters?
- Trade-offs explained
- Recommendations provided
- Defaults justified
-
Are prerequisites obvious?
- Installation instructions
- API keys/accounts
- File size warnings
Common Patterns by Tool Type
API Query Tools
"description": "Query [data type] from [source]. [Prerequisites]. Filter by [criteria]. Returns [output]. [Data scale]. Use for: [discovery], [analysis], [specific research tasks]."
Key elements:
- What you're querying
- How to filter
- What you get back
- Scale of data
- Prerequisites
Data Download Tools
"description": "Download [file types] from [source]. [Format details]. [File size warning]. [Authentication requirement]. Use for: [offline analysis], [custom processing], [integration]."
Key elements:
- File formats available
- Size warning
- Authentication needs
- What's in the files
Enrichment/Analysis Tools
"description": "Analyze [input type] to find [results]. **Required: Provide ONE input type** - (1) [option], (2) [option], (3) [option]. Compares against [database/background]. [Result format]. Use for: [identifying], [discovering], [predicting]."
Key elements:
- Input requirements clear
- Options numbered
- What gets compared
- What you learn
Validation Commands
After updating descriptions, validate JSON syntax:
# Validate all tool JSONs
python3 -m json.tool src/tooluniverse/data/your_tools.json > /dev/null && echo "✓ Valid"
# Check all tools in category
for f in src/tooluniverse/data/*_tools.json; do
python3 -m json.tool "$f" > /dev/null && echo "✓ $f valid" || echo "✗ $f invalid"
done
Example: Before and After
Before (Unclear):
{
"name": "Tool_enrichment",
"description": "Perform enrichment with tool to find factors.",
"parameter": {
"properties": {
"bed": {"description": "BED data"},
"motif": {"description": "Motif"},
"genes": {"description": "Genes"},
"threshold": {"description": "Threshold value"}
}
}
}
After (Clear):
{
"name": "Tool_enrichment_analysis",
"description": "Identify transcription factors enriched in your data. **Required: Provide ONE input type** - (1) BED genomic regions, (2) DNA sequence motif (IUPAC notation), or (3) gene symbol list. Compares against 400,000+ ChIP-seq experiments. Returns ranked proteins with enrichment scores. Note: Returns submission URL (web-based analysis). Use for: identifying regulators of regions, finding proteins bound to motifs, discovering transcription factors regulating genes.",
"parameter": {
"properties": {
"bed_data": {
"description": "**Option 1**: BED format regions (tab-separated: chr, start, end). For finding proteins bound to genomic regions. Example: 'chr1\\t1000\\t2000'."
},
"motif": {
"description": "**Option 2**: DNA motif in IUPAC notation (A/T/G/C, W=A|T, S=G|C, M=A|C, K=G|T, R=A|G, Y=C|T). Example: 'CANNTG' (E-box)."
},
How to use devtu-optimize-descriptions on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add devtu-optimize-descriptions
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches devtu-optimize-descriptions from GitHub repository mims-harvard/tooluniverse and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate devtu-optimize-descriptions. Access the skill through slash commands (e.g., /devtu-optimize-descriptions) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★41 reviews- ★★★★★Charlotte Mehta· Dec 28, 2024
Registry listing for devtu-optimize-descriptions matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kaira Bansal· Dec 12, 2024
devtu-optimize-descriptions reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kaira Ghosh· Dec 8, 2024
We added devtu-optimize-descriptions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kofi Nasser· Nov 27, 2024
devtu-optimize-descriptions reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Nia Robinson· Nov 19, 2024
devtu-optimize-descriptions fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Li Lopez· Oct 18, 2024
Registry listing for devtu-optimize-descriptions matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kofi Garcia· Oct 10, 2024
We added devtu-optimize-descriptions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Li Ndlovu· Sep 25, 2024
Useful defaults in devtu-optimize-descriptions — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Sep 17, 2024
We added devtu-optimize-descriptions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aanya Mehta· Sep 13, 2024
We added devtu-optimize-descriptions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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