# Sourabh

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Sourabh Nyalkalkar](https://cerebralvalley.ai/u/sourabhn)
- **GitHub:** https://github.com/sourabhnk/mosaic-mcp
- **Demo video:** https://youtu.be/2WTIgS9viGQ
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/54

Mosaic is an MCP server for preclinical target diligence. Our user — a comp-bio at a 10–30 person biotech, or a solo diligence consultant — gets asked "which of these five targets deserves the next six months of budget?" and answers it by hand: PubMed, ChEMBL, Lens, DepMap, ClinicalTrials.gov, three days to two weeks per target. The real problem isn't scattered data; it's that they deliver the verdict without being able to see their own blind spots. Open Targets shows data but takes no position; Causaly is enterprise-priced; a raw LLM invents confident answers with no provenance. Mosaic runs inside their existing Claude session, composes across sources via MCP without touching proprietary data, and returns a coverage-honest verdict — one that states confidence per dimension and explicitly flags where its coverage is too thin to judge. For a budget decision, a system that tells you where it's blind beats one that just sounds sure.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/54. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt