tooluniverse-protein-structure-retrieval▌
mims-harvard/tooluniverse · updated Apr 8, 2026
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Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
Protein Structure Data Retrieval
Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
IMPORTANT: Always use English terms in tool calls. Respond in the user's language.
LOOK UP DON'T GUESS: Never assume PDB IDs, resolution, or availability. Always query RCSB/PDBe and AlphaFold to confirm.
Domain Reasoning
Not all structures are equal. X-ray <2 A is high-quality for drug design. Cryo-EM 3-4 A is good for fold but not side chains. AlphaFold is excellent for well-folded domains but unreliable for disordered regions. Always check pLDDT (AlphaFold) or resolution (experimental) before drawing conclusions.
Workflow
Phase 0: Clarify (if needed) → Phase 1: Disambiguate Protein → Phase 2: Retrieve Structures → Phase 3: Report
Phase 0: Clarification (When Needed)
Ask ONLY if: protein name ambiguous (e.g., "kinase"), organism not specified, unclear if experimental vs AlphaFold needed. Skip for: specific PDB IDs, UniProt accessions, unambiguous protein+organism.
Phase 1: Protein Disambiguation
# By PDB ID: direct retrieval
# By UniProt: get AlphaFold + search experimental structures
af_structure = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)
# By protein name: search
result = tu.tools.PDBeSearch_search_structures(protein_name=protein_name)
Identity Checklist
- Protein name/gene identified, organism confirmed
- UniProt accession (if available), isoform/variant specified (if relevant)
Phase 2: Data Retrieval (Internal)
Retrieve silently. Do NOT narrate the process.
pdb_id = "4INS"
# Search, metadata, quality, ligands, similar structures
result = tu.tools.PDBeSearch_search_structures(protein_name=name)
metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
exp = tu.tools.RCSBData_get_entry(pdb_id=pdb_id)
quality = tu.tools.PDBeValidation_get_quality_scores(pdb_id=pdb_id)
ligands = tu.tools.PDBe_KB_get_ligand_sites(pdb_id=pdb_id)
similar = tu.tools.PDBeSIFTS_get_all_structures(pdb_id=pdb_id, cutoff=2.0)
# PDBe additional data
summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id)
molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)
# AlphaFold (when no experimental structure, or for comparison)
af = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)
Fallback Chains
| Primary | Fallback |
|---|---|
| RCSB search | PDBe search |
| get_protein_metadata | pdbe_get_entry_summary |
| Experimental structure | AlphaFold prediction |
| get_protein_ligands | PDBe_KB_get_ligand_sites |
Phase 3: Report Structure Profile
Present as a Structure Profile Report. Hide search process. Include:
- Search Summary: query, organism, experimental + AlphaFold structure counts
- Best Structure: PDB ID, UniProt, organism, method, resolution, date, quality assessment
- Experimental Details: method, resolution, R-factor, R-free, space group
- Composition: chains, residues (coverage%), ligands, waters, metals
- Bound Ligands: ligand ID, name, type, binding site
- Binding Site Details (for drug discovery): location, key residues, druggability
- Alternative Structures: ranked by quality with resolution, method, ligands
- AlphaFold Prediction: UniProt, model version, pLDDT confidence distribution, use cases
- Structure Comparison: resolution, completeness, ligands across structures
- Download Links: PDB/mmCIF/AlphaFold formats, database URLs
Quality Assessment
Experimental Structures
| Tier | Criteria |
|---|---|
| Excellent | X-ray <1.5A, complete, R-free <0.22 |
| High | X-ray <2.0A OR Cryo-EM <3.0A |
| Good | X-ray 2.0-3.0A OR Cryo-EM 3.0-4.0A |
| Moderate | X-ray >3.0A OR NMR ensemble |
| Low | >4.0A, incomplete, or problematic |
Resolution Use Cases
<1.5A: atomic detail, H-bond analysis. 1.5-2.0A: drug design. 2.0-2.5A: structure-based design. 2.5-3.5A: overall architecture. >3.5A: domain arrangement only.
AlphaFold Confidence (pLDDT)
90: very high, experimental-like. 70-90: good backbone. 50-70: uncertain/flexible. <50: likely disordered.
Error Handling
| Error | Response |
|---|---|
| "PDB ID not found" | Verify 4-char format, check if obsoleted |
| "No structures" | Offer AlphaFold, suggest similar proteins |
| "Download failed" | Retry once, provide alternative link |
| "Resolution unavailable" | Likely NMR/model, note in assessment |
Tool Reference
RCSB PDB: PDBeSearch_search_structures (search), get_protein_metadata_by_pdb_id (basic info), RCSBData_get_entry (details), PDBeValidation_get_quality_scores (quality), PDBe_KB_get_ligand_sites (ligands), PDBeSIFTS_get_all_structures (homologs)
PDBe: pdbe_get_entry_summary (overview), pdbe_get_entry_molecules (entities), pdbe_get_experiment_info (experimental), PDBe_KB_get_ligand_sites (pockets)
AlphaFold: alphafold_get_prediction (get prediction), alphafold_get_summary (search)
How to use tooluniverse-protein-structure-retrieval 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 tooluniverse-protein-structure-retrieval
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tooluniverse-protein-structure-retrieval 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 tooluniverse-protein-structure-retrieval. Access the skill through slash commands (e.g., /tooluniverse-protein-structure-retrieval) 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
4.7★★★★★38 reviews- ★★★★★Ganesh Mohane· Dec 12, 2024
I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Nov 3, 2024
Useful defaults in tooluniverse-protein-structure-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Chaitanya Patil· Oct 22, 2024
tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Diya Flores· Sep 25, 2024
tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yuki Taylor· Sep 25, 2024
I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Layla Malhotra· Sep 9, 2024
tooluniverse-protein-structure-retrieval fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Rahul Santra· Sep 1, 2024
Solid pick for teams standardizing on skills: tooluniverse-protein-structure-retrieval is focused, and the summary matches what you get after install.
- ★★★★★Amina Choi· Aug 28, 2024
Registry listing for tooluniverse-protein-structure-retrieval matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Aug 20, 2024
We added tooluniverse-protein-structure-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Nikhil Lopez· Aug 16, 2024
Useful defaults in tooluniverse-protein-structure-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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