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)

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 “View Advisory”.
• Problems panel: security diagnostics with severity.
• Agent hints: prefer safe alternatives when available.

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Semantic Annotations for Library APIs

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: “Use @serialize APIs” 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

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

Concept:

• Document JSON-RPC API with schemas and examples.
• Generate synthetic interaction traces for fine-tuning / tool-use evaluation.
• Add an MCP server wrapper that exposes current agent methods as MCP tools/resources.

Candidate Deliverables:

• docs/AGENT_API.md with request/response schemas
• Example flows: read AST ? mutate ? verify
• MCP bridge module (optional): maps JSON-RPC to MCP tools

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