# PhaseFolio

> PhaseFolio — phasefolio-mcp. Use this tool when you need to accurately value early-stage biotech assets, assess their probability of success, and analyze competitive landscapes. It solves problems related to asset valuation, risk assessment, and competitive analysis, providing inputs such as project metadata, scenario inputs, and evidence registers, and outputs like rNPV, cumulative probability of success, and asset-anchored competitive landscapes. Use PhaseFolio in contexts where data-driven decision-making is crucial, such as healthcare investing, tech transfer, and biotech research.

Canonical page: https://skillsregistry.net/skills/phasefolio-mcp  
JSON: https://api.skillsregistry.net/v1/skills/phasefolio-mcp

## Description

**Biotech rNPV / probability-of-success engine for AI agents.** Audit-grade asset valuation with signed dossier exports, cell-level evidence registers, and asset-anchored competitive landscape — the same engine healthcare investors and tech transfer offices use to value early-stage biotech assets, exposed directly to your AI analyst.

### Tools

**Public tier — no auth required**

- `query_benchmarks` — anonymized network statistics (PoS by indication × modality, cost / duration percentiles)
- `verify_export` — verify a signed PhaseFolio dossier by content hash or URL; returns issued timestamp, methodology version, originating-org identifier
- `get_methodology` — fetch any methodology section in citable form (backtest, PoS calibration, IRA framework, evidence standards, network benchmarks)

**Bearer tier — request a token at [app.phasefolio.com/contact](https://app.phasefolio.com/contact)**

- `get_project` — project metadata: indication, sub-indication, modality, biomarker, asset name, stage at entry
- `list_scenarios` — scenarios in a project with top-line eNPV / rNPV
- `get_scenario` — full scenario inputs + computed outputs (eNPV, rNPV, cumulative PoS, per-stage breakdown, top sensitivity drivers)
- `get_evidence` — evidence-register entries: citations, sources, supporting documents, freshness
- `get_dossier` — structured dossier JSON, mirroring the IC Dossier PDF / Excel exports
- `query_landscape` — asset-anchored competitive landscape (comparable trials, competing programs, sponsor activity, biomarker overlap) sourced from CT.gov + FDA + curated enrichment

### Trust artifacts

Every PhaseFolio dossier export carries a cryptographic signature tying file content to engine version and methodology version. AI agents can verify any artifact in one tool call. Public key at [`/.well-known/phasefolio-pubkey.pem`](https://app.phasefolio.com/.well-known/phasefolio-pubkey.pem).

Methodology is fully published and citation-ready at [app.phasefolio.com/methodology](https://app.phasefolio.com/methodology). Backtested on a held-out cohort: 16 rheumatoid-arthritis programs, AUC 0.625, Wilson 95% CI on accuracy. Oncology backtest in progress.

### Sources
- [Methodology hub](https://app.phasefolio.com/methodology)
- [Verify a signed export](https://app.phasefolio.com/verify)
- [Official MCP Registry listing](https://registry.modelcontextprotocol.io/v0/servers?search=phasefolio)

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** finance
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/phasefolio/mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "phasefolio-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/phasefolio-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/phasefolio-mcp/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt