devtu-create-tool▌
mims-harvard/tooluniverse · updated Apr 8, 2026
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Create new scientific tools following established patterns.
ToolUniverse Tool Creator
Create new scientific tools following established patterns.
Top 7 Mistakes (90% of Failures)
- Missing
default_config.pyEntry — tools silently won't load - Non-nullable Mutually Exclusive Parameters — validation errors (#1 issue in 2026)
- Fake test_examples — tests fail, agents get bad examples
- Single-level Testing — misses registration bugs
- Skipping
test_new_tools.py— misses schema/API issues - Tool Names > 55 chars — breaks MCP compatibility
- Raising Exceptions — should return error dicts instead
Two-Stage Architecture
Stage 1: Tool Class Stage 2: Wrappers (Auto-Generated)
@register_tool("MyTool") MyAPI_list_items()
class MyTool(BaseTool): MyAPI_search()
def run(arguments): MyAPI_get_details()
One class handles multiple operations. JSON defines individual wrappers. Need BOTH.
Three-Step Registration
Step 1: Class registration via @register_tool("MyAPITool")
Step 2 (MOST COMMONLY MISSED): Config registration in default_config.py:
TOOLS_CONFIGS = {
"my_category": os.path.join(current_dir, "data", "my_category_tools.json"),
}
Step 3: Automatic wrapper generation on tu.load_tools()
Implementation Guide
Files to Create
src/tooluniverse/my_api_tool.py— implementationsrc/tooluniverse/data/my_api_tools.json— tool definitionstests/tools/test_my_api_tool.py— tests
Python Tool Class (Multi-Operation Pattern)
from typing import Dict, Any
from tooluniverse.tool import BaseTool
from tooluniverse.tool_utils import register_tool
import requests
@register_tool("MyAPITool")
class MyAPITool(BaseTool):
BASE_URL = "https://api.example.com/v1"
def __init__(self, tool_config):
super().__init__(tool_config)
self.parameter = tool_config.get("parameter", {})
self.required = self.parameter.get("required", [])
def run(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
operation = arguments.get("operation")
if not operation:
return {"status": "error", "error": "Missing: operation"}
if operation == "search":
return self._search(arguments)
return {"status": "error", "error": f"Unknown: {operation}"}
def _search(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
query = arguments.get("query")
if not query:
return {"status": "error", "error": "Missing: query"}
try:
response = requests.get(
f"{self.BASE_URL}/search",
params={"q": query}, timeout=30
)
response.raise_for_status()
data = response.json()
return {"status": "success", "data": data.get("results", [])}
except requests.exceptions.Timeout:
return {"status": "error", "error": "Timeout after 30s"}
except requests.exceptions.HTTPError as e:
return {"status": "error", "error": f"HTTP {e.response.status_code}"}
except Exception as e:
return {"status": "error", "error": str(e)}
JSON Configuration
[
{
"name": "MyAPI_search",
"class": "MyAPITool",
"description": "Search items. Returns array of results. Supports Boolean operators. Example: 'protein AND membrane'.",
"parameter": {
"type": "object",
"required": ["operation", "query"],
"properties": {
"operation": {"const": "search", "description": "Operation (fixed)"},
"query": {"type": "string", "description": "Search term"},
"limit": {"type": ["integer", "null"], "description": "Max results (1-100)"}
}
},
"return_schema": {
"oneOf": [
{"type": "object", "properties": {"data": {"type": "array"}}},
{"type": "object", "properties": {"error": {"type": "string"}}, "required": ["error"]}
]
},
"test_examples": [{"operation": "search", "query": "protein", "limit": 10}]
}
]
Critical Requirements
- return_schema MUST have oneOf: success + error schemas
- test_examples MUST use real IDs: NO "TEST", "DUMMY", "PLACEHOLDER"
- Tool name <= 55 chars:
{API}_{action}_{target}template - Description 150-250 chars: what, format, example, notes
- NEVER raise in run(): return
{"status": "error", "error": "..."} - Set timeout on all HTTP requests (30s)
- Standard response:
{"status": "success|error", "data": {...}}
Parameter Design
Mutually Exclusive Parameters (CRITICAL — #1 issue)
When tool accepts EITHER id OR name, BOTH must be nullable:
{
"id": {"type": ["integer", "null"], "description": "Numeric ID"},
"name": {"type": ["string", "null"], "description": How to use devtu-create-tool 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-create-tool
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches devtu-create-tool 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-create-tool. Access the skill through slash commands (e.g., /devtu-create-tool) 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
Submit your Claude Code skill and start earning
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
4.6★★★★★32 reviews- ★★★★★Neel Menon· Dec 24, 2024
Solid pick for teams standardizing on skills: devtu-create-tool is focused, and the summary matches what you get after install.
- ★★★★★Neel Rao· Dec 20, 2024
devtu-create-tool reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ishan Khanna· Dec 4, 2024
devtu-create-tool is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Neel Thomas· Nov 15, 2024
Registry listing for devtu-create-tool matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Wang· Nov 15, 2024
We added devtu-create-tool from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Abebe· Oct 6, 2024
Useful defaults in devtu-create-tool — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Isabella Thompson· Oct 6, 2024
Keeps context tight: devtu-create-tool is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Sep 25, 2024
Useful defaults in devtu-create-tool — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Jin Rao· Sep 21, 2024
Solid pick for teams standardizing on skills: devtu-create-tool is focused, and the summary matches what you get after install.
- ★★★★★Jin Patel· Sep 13, 2024
We added devtu-create-tool from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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