backend software
interview platform
Three related tools for career evidence and interview practice, with explicit storage contracts and coding endpoints between them.
19 MCP tools · markdown and SQLite backends
- my contribution
- Built Recall-Seed, STARfolio, and Scroll; defined storage and endpoint contracts.
- project status
- Implemented · active personal projects
STARfoliodesktop interviews + SQLite search
Scrollcoding endpoints + grading callbacks
the problem
interview practice needs concrete evidence about what someone has done. i wanted career notes, retrieval, and coding exercises to work across tools without coupling every feature to one desktop app.
distinct responsibilities
Recall-Seed is a C#/.NET MCP server. its 19 tools retrieve and store career evidence, check citation identifiers, and seed coding challenges into Scroll. the calling model generates language; the server supplies source material and validation.
STARfolio is the Electron desktop surface for career notes and mock interviews. SQLite FTS5 and sqlite-vec support keyword and vector search.
Scroll is an implemented editor with sync server and agent-facing endpoints. relative viewport anchoring keeps edits above the visible region from moving the reader’s screen.
backend decisions
one IExperienceStore interface supports markdown-vault and SQLite implementations. tools use that interface rather than knowing how each backend stores notes. the SQLite path uses WAL mode and a stable read/write contract.
coding challenges cross a separate boundary: Recall-Seed provides the problem schema; Scroll hosts the editor and grader, then posts the verdict back with a correlation identifier.
verification and limits
Recall-Seed CI builds Release, runs xUnit tests, and exercises the stdio protocol from initialization through a tool call. source-linked example runs show how thin evidence produces a gap question.
citation checks verify that referenced identifiers exist. they are not a guarantee that every generated statement is true.
Scroll’s treap has differential property tests against a simpler reference structure. a targeted sweep of 20 hand-built mutants reached near-total detection; the separate whole-suite mutation run reported 86–95% on covered code. those measurements assess test sensitivity, not production reliability.