An API that catches what your LLM confidently got wrong
Six specialized endpoints including causality graphs for grounding AI outputs.
AI bug finder and code analyzer
Forces LLMs to debug with AST evidence instead of pattern-matching symptoms.
Developers using AI coding assistants for debugging
Cursor · Continue · Sourcegraph Cody
Six specialized endpoints including causality graphs for grounding AI outputs.
Token-level streaming halt stops hallucinations mid-sentence before user sees them—genuinely novel safety layer.
The project maps the entire OAuth/MCP discovery-to-DCR funnel and gives actionable failure points — e.g., missing WWW-Authenticate headers, malformed PRM or issuer metadata, or broken token endpoints. It’s a focused, practical CLI that also fits into CI (GitHub Actions badge, quickscan command), so teams can catch auth regressions before rollout. Niche but very useful if you run or validate MCP/OAuth endpoints; wider adoption will depend on more examples and integration templates.
Six endpoints for grounding AI, but RAG solutions already handle this.
It turns real program analysis — ASTs, cross-file dependency graphs, taint tracking and Z3 symbolic paths — into callable MCP tools for agents, not just another prettier linter. Concrete features like simulate_refactor, generate_unit_tests from symbolic paths, and cross-file security_scan give it a distinct technical voice. The <10% false-positive claim and heavy test coverage are promising, but I'd want to see results on large, messy repos before swapping out existing scanners.
AST parser catches missing env vars before you deploy to production.