Q (haiku): Can you help me write a production-ready Python script using Bokeh to build an interactive clinicogenomic dashboard to analyzing the MSK-CHORD dataset? I have some specific requirements I can give you

▶ list_studies
{
 "search": "MSK CHORD",
 "verbose": "true"
}

✗ error
<tool_use_error>Error: No such tool available: mcp__navigator__list_studies</tool_use_error>

▶ list_studies
{
 "search": "MSK CHORD",
 "verbose": true
}

◀ result
{"result":[{"cancer_study_identifier":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","description":"Targeted sequencing of 25040 tumors from 24950 patients and their matched normals via MSK-IMPACT, along with clinical annotations, some of which are derived from natural language processing (denoted NLP). This data is available under the <a href=\"https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en\">Creative Commons BY-NC-ND 4.0 license</a>. For commercial use, please contact <a href=\"mailto: datarequests@mskcc.org\">datarequests@mskcc.org</a>","type_of_cancer_id":"mixed","sample_count":25040,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024"}]}

═ answer (success)
Perfect! MSK-CHORD is available. Now, **please share your specific requirements** so I can build exactly what you need. Here are some typical questions:

**Dashboard Scope:**
1. **Which clinical features** do you want to visualize? (e.g., cancer types, mutations in specific genes like TP53/KRAS, tumor grade, stage, treatment response)
2. **Which molecular profiles** are most important? (mutations, CNAs, fusions)
3. **Key analyses** — what questions should the dashboard answer?
   - Mutation frequency by cancer type?
   - Survival analysis (OS, DFS)?
   - Comorbidity patterns?
   - Treatment outcomes?
   - Subgroup comparisons?

**Technical Preferences:**
4. **Data volume** — single cancer type or pan-cancer?
5. **Interactivity** — filter-and-drill-down (StudyView-style), linked plots, or custom comparisons?
6. **Deployment** — Bokeh server app, static HTML file, or embedded in a larger platform?
7. **Performance** — acceptable query time (seconds to minutes)?

**Data Integration:**
8. Should it query **live from ClickHouse** (requires credentials) or use **pre-exported CSVs** (snapshot-based)?

Once you provide these details, I'll write a modular, well-documented script with:
- Proper error handling and logging
- Efficient ClickHouse queries (or CSV loading)
- Production-ready Bokeh layouts
- Responsive interactivity
- Unit tests and configuration management

What are your priorities?