devtu-auto-discover-apis - Claude MCP Skill

# Automated Life Science API Discovery & Tool Creation

Discover, create, validate, and integrate life science APIs into ToolUniverse.

## Four-Phase Workflow

```
Gap Analysis → API Discovery → Tool Creation → Validation → Integration
     ↓              ↓               ↓              ↓            ↓
  Coverage      Web Search      devtu-create   devtu-fix    Git PR
```

Human approval gates after: discovery, creation, validation, and before PR.

---

## Phase 1: Discovery & Gap Analysis

### 1.1 Analyze Current Coverage
Load ToolUniverse, categorize tools by domain (genomics, proteomics, drug discovery, clinical, omics, imaging, literature, pathways, systems biology). Count per category.

### 1.2 Identify Gap Domains
- **Critical Gap**: <5 tools in category
- **Moderate Gap**: 5-15 tools, missing key subcategories
- **Emerging Gap**: New technologies not represented

Common gaps: single-cell genomics, metabolomics, patient registries, microbial genomics, multi-omics integration, synthetic biology, toxicology.

### 1.3 Web Search for APIs
For each gap domain, run multiple queries:
1. `"[domain] API REST JSON"` — direct API search
2. `"[domain] public database"` — database discovery
3. `"[domain] API 2025 OR 2026"` — recent releases
4. `"[domain] database" site:nar.oxfordjournals.org` — NAR Database Issue

Extract: base URL, endpoints, auth method, parameter schemas, rate limits.

### 1.4 Score and Prioritize

| Criterion | Max Points |
|-----------|------------|
| Documentation Quality | 20 |
| API Stability | 15 |
| Authentication Simplicity | 15 |
| Coverage | 15 |
| Maintenance | 10 |
| Community | 10 |
| License | 10 |
| Rate Limits | 5 |

High priority (>=70), Medium (50-69), Low (<50).

### 1.5 Generate Discovery Report
Coverage analysis, prioritized candidates with scores, implementation roadmap.

---

## Phase 2: Tool Creation

For each API, use `Skill(skill="devtu-create-tool")` or follow these patterns.

### Architecture Decision
- Multiple endpoints → multi-operation tool (single class, multiple JSON wrappers)
- Single endpoint → single-operation acceptable

### Key Steps
1. Design tool class following template — see [references/tool-templates.md](references/tool-templates.md)
2. Create JSON config with oneOf return_schema
3. Find real test examples (use List endpoint → extract IDs → verify)
4. Register in `default_config.py`

### Critical Requirements
- return_schema MUST have `oneOf` (success + error schemas)
- test_examples MUST use real IDs (NO placeholders)
- Tool name <= 55 characters
- NEVER raise exceptions in `run()` — return error dict
- Set timeout on all HTTP requests (30s)

---

## Phase 3: Validation

Full guide: [references/validation-guide.md](references/validation-guide.md)

### Quick Validation Checklist
1. **Schema**: oneOf structure, data wrapper, error field
2. **Placeholders**: No TEST/DUMMY/PLACEHOLDER in test_examples
3. **Loading**: 3-step check (class registered, config registered, wrappers generated)
4. **Integration tests**: `python scripts/test_new_tools.py [api_name] -v` → 100% pass

Fix failures with `Skill(skill="devtu-fix-tool")`.

---

## Phase 4: Integration

Use `Skill(skill="devtu-github")` or:
1. Create branch: `feature/add-[api-name]-tools`
2. Stage tool files + default_config.py
3. Commit with descriptive message
4. Push and create PR with validation results

---

## Processing Patterns

| Pattern | When to Use |
|---------|------------|
| **Batch** (multiple APIs → single PR) | Same domain, similar structure |
| **Iterative** (one API at a time) | Complex auth, novel patterns |
| **Discovery-only** (report, no tools) | Planning roadmap |
| **Validation-only** (audit existing) | PR review, quality check |

---

## References

- **Tool templates** (Python class + JSON config): [references/tool-templates.md](references/tool-templates.md)
- **Validation & integration guide**: [references/validation-guide.md](references/validation-guide.md)