Returns a summary of all tables and their columns in the ChaosLimba database. Use this to orient yourself when first connecting.

Returns all entries from grammar_feature_map, optionally filtered by CEFR level. Shows feature keys, names, categories, descriptions, prerequisites, and sort order.

For a given grammar feature, returns its full recursive prerequisite chain so you can audit whether the sequencing is pedagogically sound. Caps depth at 10.

Returns content items, optionally filtered by difficulty level, topic, or type. When no difficulty filter is set, results are stratified across difficulty levels for even coverage.

Cross-references grammar_feature_map against content_items.language_features to show which grammar features have content coverage and which are gaps. The core instructional design audit tool.

Inserts a new content item into the content_items table. Use during dev sessions to seed reading passages, audio content, etc.

Returns aggregated error patterns from error_logs across all users (anonymized). Useful for understanding where learners actually struggle.

Returns a summary of fossilization interventions — which patterns are being escalated and whether they are resolving. Shows max tier reached, total interventions, and resolution counts.

Returns reading comprehension questions, optionally filtered by CEFR level. Shows passage text, question, answer options, and correct index. Useful for auditing question quality and level coverage.

Returns stress minimal pairs — words where stress placement changes meaning (e.g., CÁsă vs caSĂ). Core pronunciation training data.

Returns AI tutor conversation starter questions, optionally filtered by CEFR level or category. Useful for auditing prompt quality and topic coverage.

Returns tutor opening messages keyed by self-assessment level. Shows how the AI tutor greets learners at different proficiency levels.

Inserts a new reading comprehension question into the reading_questions table. Provide a passage, question, answer options, and the index of the correct answer.

Returns aggregated session data — session counts by type, average duration, and content engagement. All data is anonymized (no user IDs returned). Useful for understanding how learners actually use the app.

Returns proficiency score history over time — overall, listening, reading, speaking, writing scores by period. Anonymized across all users. Useful for tracking whether content improvements translate to learner gains.

Returns aggregated feature exposure data — how many times each grammar feature has been seen by learners, with correctness rates. Anonymized. Useful for finding undertaught or poorly-performing features.

Returns mystery vocabulary items — words learners encountered and flagged for exploration. Shows word, context, definition, examples, grammar info, and whether the learner has explored it. Useful for understanding organic vocabulary discovery.

Returns aggregated stats on AI-generated content — what types are being generated, for which error targets, listening rates, and estimated TTS costs. Anonymized. Useful for auditing the AI tutor's output quality and cost.

Returns AI-generated learning narrative summaries — periodic reflections on learner progress with stats. Anonymized. Useful for auditing narrative quality and checking if the reflection system captures meaningful patterns.

Returns TTS (text-to-speech) usage stats — characters consumed per day. Useful for monitoring costs and usage trends.