Rocket.Chat Code Analyzer

Graph-native code navigator for the Rocket.Chat monorepo, exposed as an MCP server. An agent answers codebase questions by declaring intent (plan), finding seeds (search), traversing the dependency graph (graph), and reading source for 1-2 key points (details) — built for high answer accuracy at low token cost on free-tier models.

Why a graph, not grep

sendMessage end-to-end crosses 5 communication layers (React tree → DDP boundary → server method → DB write → afterSave callbacks), and every cross-layer hop has no import statement. The offline indexer makes those hops traversable by extracting string-dispatch sites as virtual nodes:

Cross-layer pattern	Edge types
sdk.call('sendMessage') ↔ Meteor.methods({sendMessage})	call via virtual node
callbacks.run('afterSaveMessage') ↔ callbacks.add(...)	event_emit / event_listen
rest.get('/v1/..') ↔ API.v1.addRoute('..') (normRoute)	rest_call / rest_route
Meteor.publish ↔ Meteor.subscribe	pubsub_publish / pubsub_subscribe
new Streamer('notify-user') ↔ sdk.stream('notify-user')	stream_def / stream_sub

12 edge types total (also call/jsx/new/type static edges). Symbol collisions are resolved import-aware (pickRootFile + import distances). Ranking is graph-first, with a semantic cosine (query × file-summary vectors) fused in by RRF — see How it works below.

How it works

Two phases, one north star: zero runtime external dependency — every expensive step (parsing, summarizing, embedding, wiki generation) is amortized offline into local indexes; at query time the retriever only reads them (no external wiki service, no runtime fetch). An offline indexer turns the Rocket.Chat checkout into a graph plus a summary layer, and the self-generated wiki (wiki:gen) doubles as a routing layer; the online retriever walks all of it to answer one question at a time, through four agent tools.

Offline — build the index (prewarm, chained by refresh)

1. scan + incremental hash — walk the source tree; hasher fingerprints each file so a rebuild only re-processes what changed.
2. dehydrate (ts-morph) — skeleton.ts parses each file into a skeleton (symbols + signatures); extract-edges.ts pulls the 12 edge types — 4 static (call/jsx/new/type) and 8 string-dispatch ones (event_*, pubsub_*, rest_*, stream_*) captured as virtual nodes, so the no-import cross-layer hops become traversable.
3. build GLOBAL_INDEX — the in-memory graph: symbols (name → files) · callGraph (reverse edges: caller → {file, edgeType}) · fileDependents · allFiles.
4. summarize + embed — summarize.ts writes one paragraph per file; embed.ts turns each summary into a vector. This is the semantic layer the ranker blends in below — no source is re-read at query time except the final details step.

Online — answer one question (the four tools)

5. plan(question) → intent — classifyIntent keyword-matches the question to one of six intents (architecture / routing / locate / pattern / call-chain / impact), each mapped to a default graph move + depth by RECIPES. In parallel, entry-map reads the self-generated wiki (the routing layer) and runs one zero-LLM offline walk (entry graph → pick page → pick seed → affinity walk), handing the agent a candidate-file map to start from — in-memory, no LLM, no network.
6. search(query) → seeds — three deterministic lookups, no ranking: exact symbol hit (🎯), file-path fragment (📁), and a content grep fallback (🔍) for call-patterns and index misses.
7. graph(query, move, depth) → traverse + rank — the heart of retrieval:
   • expand — lexicalSeeds (fuzzysort over symbol names) seeds a BFS neighborhood, then ranks by fusing two signals with RRF: (1) a structural score 2·proximity + 1.5·lexical + 0.6·cohesion + 0.2·centrality + prior − 0.6·hubPenalty − testPenalty (close to the seed, densely wired into the neighborhood, a real definition — minus the everything-touches utilities), and (2) semantic cosine (query vector × the symbol's file-summary vector). The two are complementary — semantic catches on-topic summaries, fuzzy catches path-literal matches — so RRF takes whichever ranks a file higher rather than a weighted sum that pleases neither (litmus: 7→14 top-25 hits). No vectors present → pure structure, bit-identical to the old behavior.
   • down — ordered callee tree (call chains); up — layered dependents including dynamic edges (blast radius).
8. details(symbol, file) → read — source.ts re-opens the one located file with ts-morph and returns the symbol's real source, so every citation is grounded, never recalled from training data.

Net: fuzzysort seeds + graph arithmetic find the subsystem; the file-summary cosine re-weights what the walk already surfaced; ts-morph reads ground truth only at the two ends — the index build, and the final details.

Layout

src/
  config.ts                    ROCKET_CHAT_SRC (default ../Rocket.Chat), output paths
  architecture.json            entry-point hints keyed by query keywords
  indexer/                     knowledge layer: scan → dehydrate (ts-morph) → hash → build → load
    state.ts                   GLOBAL_INDEX: symbols · callGraph (reverse edges) · fileDependents · allFiles
    skeleton.ts hasher.ts local-db.ts index.ts
  server/                      retrieval layer (control / data split)
    index.ts                   MCP bootstrap
    registry.ts                tool schemas + dispatch + metering (MCP adapter only)
    session.ts                 SESSION: declared intent + call metering
    intent.ts                  intent → move/depth recipes + keyword classifier
    tools/                     one tool per file
      plan.ts                  control: declare question type → sets SESSION.intent
      search.ts                data: exact symbol + path fragment + grep (seeds, no ranking)
      graph.ts                 data: move = expand | down | up (defaults from intent)
      details.ts               data: full source of one located symbol (guarded)
    engine/                    one algorithm per file
      seeds.ts                 lexicalSeeds — fuzzysort seeding only
      expand.ts                expandNeighborhood — BFS + the ONLY ranking formula
      down.ts                  ordered callee tree (call chains)
      up.ts                    layered dependents / blast radius (impact)
      source.ts                ts-morph source extraction
      common.ts                shared helpers (layers, test filters, root picking, arch hints)
  eval/                        measurement layer
    gen.ts                     generate agent answers: --mode=mcp [--oracle] [--model] [--filter] (nomcp/wiki-only retired 2026-07-08)
    trace.ts                   per-question retrieval trace (record-only) → logs/data/retrieval-trace/
    report.ts                  single report → logs/reports/report.md (对不对 gold: scope/召回/walk-core/seed + trace; semantic via --semantic)
    judge.ts                   semantic-compare LIBRARY (judgeAnswers; used by report --semantic; gold = answers-claude, never the wiki)
    truth.ts                   extract Claude-derived ground truth → utils/claude-truth.json (core/supporting/chain, one-time)
    utils/                     testcases.json + shared scoring helpers
  wiki/                        knowledge product: self-generated architecture wiki (feeds the `wiki` tool + wiki-site)
    generate.ts                5-step pipeline: outline → guide → write → diagram → verify (→ data/wiki-map.json, git-sha stamped)
    outline.ts guide.ts write.ts diagram.ts citations.ts verify.ts   taxonomy.ts tree.ts route.ts families.ts
data/
  wiki-map.json                generated wiki (page structure + component relations + index-ranked files) — read by the `wiki` tool & wiki-site
  index/                       chunks.json · chunk-vectors.json · module-graph.json
wiki-site/                     static wiki viewer (index.html · app.js · style.css) over data/*.json — `npm run wiki:serve`
logs/reports/
  report.md                    single report — 对不对(gold, zero-API) + trace + optional semantic; by report.ts
  wiki-verify.md               self-generated wiki citation validity (by wiki:verify)

Tools (agent-facing)

Tool	Role	Returns
plan(question)	control — classify intent	strategy + default graph move/depth
search(query, layer?)	seeds	exact symbol hits · path matches · grep fallback
graph(query, move?, depth?, …)	traverse	expand ranked neighborhood · down ordered chain · up blast radius
details(symbolName, filename)	read	full source (class methods via Class.method)

Intent recipes: architecture/routing → expand (depth 2) · locate/pattern → expand (depth 1) · call-chain → down (depth 5) · impact → up (depth 5). The agent can override any default.

Run

The index must be built (prewarm) before the MCP server, gen:mcp, or trace — they all read GLOBAL_INDEX. refresh chains the whole pipeline in the right order.

# --- index + server (index needs a Rocket.Chat checkout at ../Rocket.Chat or $ROCKET_CHAT_SRC) ---
npm run prewarm     # build/load the graph index (run first)
npm run start       # MCP server on stdio
npm run inspect     # MCP inspector against the server

# --- eval pipeline (GEMINI_API_KEY in .env for gen:mcp; CLAUDE_API_KEY for --semantic/truth) ---
npm run gen:mcp     # agent answers: Gemini + plan/search/graph/details + self-generated `wiki` self-loop
                    #   → logs/answers-gemini-mcp-selfloop/  (add --oracle to force intent from testcase type)
                    #   Gemini free-tier throws ~10% transient 404/429/5xx — gen.ts retries w/ backoff (retries don't affect determinism)
npm run trace       # deterministic per-question retrieval trace, no API → logs/data/retrieval-trace/
npm run report      # single report → logs/reports/report.md  (对不对 gold: scope/召回/walk-core/seed + trace; zero-API)
                    #   add `-- --semantic` for the paid Claude semantic segment (agent answers vs answers-claude gold)
npm run truth       # (re)build Claude-derived ground truth from answers-claude → src/eval/utils/claude-truth.json (paid API; rerun only when answers-claude changes)

# --- self-generated architecture wiki (paid LLM: outline/guide/write/diagram/verify) ---
npm run wiki:gen    # 5-step pipeline → data/wiki-map.json (stamped with the indexed git sha)
npm run wiki:serve  # static viewer at http://localhost:8080 (whitelist: only wiki-site/ + data/*.json)

npm run refresh     # one-shot chain: prewarm → module:build → summaries → embeds → module:summarize → wiki:map → gen:mcp → trace → report
                    #   (retrieval/report are deterministic; gen:mcp + summaries call LLM APIs; --semantic is separate/paid)

# --- unit guards ---
npm test            # SYSTEM_PROMPT has no ground-truth path leak + gen.ts is import-side-effect-safe

Semantic scoring is opt-in and paid: run npm run report -- --semantic (uses the judge library) on demand — it's the only Claude-judged segment; refresh skips it.

Evaluation discipline

Two questions, never conflated: did the answer come out right? and where did retrieval go wrong? The semantic judge gives the verdict; the trace localizes the failure. report.md is the single artifact holding both.

• Verdict — semantic judge (opt-in, paid). npm run report -- --semantic runs the judge library (Claude sonnet-4-6, agent answers vs answers-claude/ gold) → per-question PASS / PARTIAL / FAIL, folded into report.md + cached to verdicts-latest.json. This is the only judge of final answer quality, and it's never run against the self-generated wiki (that would be circular). Latest run (Gemini + MCP, 34 cases): PASS 13 / PARTIAL 19 / FAIL 2.
• Diagnosis — trace + 对不对 (default, zero-API). A FAIL alone doesn't say why; the trace does. report.md lands the raw trace (scope → seed → walk → agent 实调 — each step's options / chosen / reason / result) next to 对不对 (trace × claude-truth.json): is the answer file's page in scope? how many answer files were recalled? did each walk step hit a core file? did the seed itself hit core, or only the wide walk? So every failure pins to a stage — scope missed the page / seed anchored wrong / walk never reached it — not just a number. A traceDrift guard voids a trace run against a stale wiki-map (its pageStep.chosen no longer matches), so stale traces can't emit fake numbers. Latest: scope 17/34 · mean recall 56%.
• Gold = src/eval/utils/claude-truth.json (extracted from answers-claude/ by truth.ts); testcases.json supplies question metadata (type/subsystem/difficulty/ordered).