whetstone_DSL/FEATURE_REQUESTS.md

# Feature Requests

> Backlog of feature ideas to triage into future sprints (e.g., Sprint 6/7).

## Security Vulnerability Awareness (Dependencies) — IMPLEMENTED (Sprint 6, Steps 190–195)

**Goal:** Warn when a dependency has known vulnerabilities and surface safer alternatives.

**Concept:**
- Maintain a vulnerability knowledge base (local cache + optional remote sources).
- When a dependency is added/updated, show immediate warnings.
- Surface findings in the Dependencies panel and Problems list.
- Optionally block auto-upgrade to vulnerable versions.

**Potential Data Sources:**
- OSV (Open Source Vulnerabilities) API / datasets
- NVD (CVE/NVD feeds)
- GitHub Security Advisories (GHSA)
- OWASP references (for categorization)

**Candidate Data Model:**
- `VulnerabilityRecord`
  - `ecosystem` (pypi/npm/crates/maven/go/vcpkg/etc)
  - `package`
  - `affected_versions`
  - `severity`
  - `summary`
  - `references`

**UI/UX:**
- Dependencies panel: inline warning badges and <20>View Advisory<72>.
- Problems panel: security diagnostics with severity.
- Agent hints: prefer safe alternatives when available.

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## Semantic Annotations for Library APIs — IMPLEMENTED (Sprint 6, Steps 193–194)

**Goal:** Tag library functions/types with semantic annotations (e.g., `@serialize`, `@crypto`, `@io`) so humans/agents can discover intent-driven APIs quickly.

**Concept:**
- Add annotation metadata for library symbols (by library + symbol).
- Attach annotations to `ExternalModule` / `TypeSignature` nodes.
- Use annotations to filter in Library Browser and guide agent completion.

**Candidate Storage:**
- `annotations/library_semanno.json` (or similar)
- Format: `{ library: { symbol: [annotations...] } }`

**UI/UX:**
- Library Browser: filter by annotation tag.
- Completion ranking: prioritize annotated matches for task keywords.
- Agent prompts: <20>Use @serialize APIs<49> guidance.

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## Notes
- Treat these as separate features to schedule independently.
- Likely Sprint 6/7, after core library-aware flow is stable.

## LLM Tooling & MCP Bridge — PLANNED (Sprint 7, Steps 202–234)

**Goal:** Make the agent API easy for LLMs to use and optionally expose it via MCP.

**Status:** Full plan written in `sprint7_plan.md`. 33 steps across 6 phases:
- Phase 7a: API documentation & JSON schemas (Steps 202–206)
- Phase 7b: MCP server with tools/resources/prompts (Steps 207–213)
- Phase 7c: Synthetic trace generation for training data (Steps 214–219)
- Phase 7d: Evaluation harness for LLM tool-use accuracy (Steps 220–224)
- Phase 7e: Model-specific tool definitions — Claude, Codex, open-source (Steps 225–229)
- Phase 7f: Session recording pipeline — capture, anonymize, export (Steps 230–234)

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