jDataMunch MCP

Composite quality grade (A–F) for a dataset (B4). Combines null severity, type-confidence, constant-column count, primary-key presence, semantic-typing coverage, and drift history into a single score with a structured breakdown. Grades structure and completeness, not whether the values are right.

Return the last N profile snapshots for a dataset. Snapshots are appended on every successful index_local — use this to detect schema/content drift over multiple ingests of the same dataset. n capped at 50.

Unified bin-counts for any column type (B8). Numeric → equal-width bins between min/max; datetime → time-bucket bins; categorical / string → top-n + 'other' bucket. Token-cheap way to ask 'what does this column look like?'. Bin counts only (default 20 bins); it never returns the underlying rows.

Forensic accounting of PII redactions for a dataset. Returns per-pattern counts from runtime_redaction_log (populated by ingest_sql_log with redact=True), so operators can verify the chokepoint is firing on production traffic. Filter by source and lookback window. Empty result with no traces ingested is not an error — it just means no scrubbing has happened yet.

Filtered row retrieval via structured filters. All filters are SQL-parameterized (no injection). Operators: eq, neq, gt, gte, lt, lte, contains, in, is_null, between. Use columns=[] to project — reduces tokens significantly on wide tables. Prefer aggregate() for summaries over paginating through rows. Returns at most limit rows (default 50); page with offset instead of raising it.

Compare schema (columns, types, nullability) between two indexed datasets. Detects added/removed columns, type changes, and null-rate shifts. Pure in-memory comparison — no re-reading source files. Useful for detecting schema changes between dataset versions. Assessment: 'identical' | 'additive' (only additions) | 'breaking' (removals or type changes).

Transitive impact of a column-level schema change (drop_column, rename_column, retype_column). Walks the inferred FK graph to max_depth, surfaces direct + transitive hits across datasets, and normalises blast_score to [0, 1]. For retype_column, also flags type_mismatch entries at FK edges whose partner type wouldn't survive the retype. Read-only.

Return cumulative token savings and cost avoided across all tool calls. Savings are modelled estimates, not per-call measurements.

Index a local data file (CSV, Excel, Parquet, or JSONL). Profiles all columns, detects types, computes statistics, and loads rows into SQLite for fast filtered retrieval. Set incremental=true (default) to skip re-indexing if file is unchanged. CSV, Excel, Parquet and JSONL only; any other format is rejected.

Index data files from a GitHub repository. Discovers CSV, Excel, Parquet, and JSONL files, downloads them, and indexes each via the same pipeline as index_local. Datasets are named {owner}--{repo}--{filename}. Max 50 MB per file, 20 files per repo. Set GITHUB_TOKEN env var for private repos or to avoid rate limits.

Ingest a SQL log file (pg_stat_statements CSV or generic JSONL, .gz transparently) into the per-dataset runtime tables. Each query is parsed for table + column refs, redacted at the chokepoint (string + numeric literals + cell-PII registry), and rolled up into runtime_query_calls keyed by (fingerprint, table, column). Tables in the log that don't match any indexed dataset count as unmapped. Foundational primitive for find_unused_columns, check_column_drop_safe, and data_health_radar (v1.6.0 sibling-parity Phase 1).

Return the version-current CLAUDE.md / AGENT.md policy snippet for jdatamunch-mcp. Lets an agent keep a one-line CLAUDE.md (e.g. "Call jdatamunch_guide and strictly follow its instructions.") instead of pasting a static snippet that drifts from the installed version. Idempotent, no dataset context required. Sibling of jcodemunch_guide and jdocmunch_guide.

Join two indexed datasets via SQL JOIN. Uses ATTACH DATABASE to combine two SQLite stores into one query. Supports inner, left, right, and cross joins. Use columns_a/columns_b to project — reduces tokens on wide tables. Row limit capped at 500. Prefer aggregate() on join results for summaries.

List every indexed dataset with its row count, column count, and source file. Call it first to find the dataset name every other tool needs, and to confirm a file was actually indexed. Lists only datasets under the active storage_path.

List GitHub repositories indexed via index_repo. Shows repo name, HEAD SHA, dataset count, total rows, and dataset names for each repo. Covers repos indexed with index_repo only; a dataset added by index_local is not listed here.

Map a natural-language intent into a ranked tool-call sequence for the given dataset (B3). Pure routing — no LLM call. Built-in intents: summarize, anomalies, compare, join, filter, trend, correlate.

Read-only sandboxed SQL escape hatch (B1). Accepts a single SELECT (or WITH … SELECT) statement. The first dataset is the main connection; additional datasets are ATTACHed under schema names (e.g. `<dataset>.rows`). Statement runs under PRAGMA query_only=1 with a 10-second budget and 500-row cap. Use this for HAVING / window functions / CTEs / multi-way joins that the structured tools don't cover.

Return a sample of rows. Useful for understanding data shape without prior knowledge. Method: 'head', 'tail', or 'random'. Use columns=[] on wide tables to reduce response size. Pass seed (int) with method='random' for deterministic, reproducible sampling. A sample shows shape, not distribution; use get_distribution when you need the spread.

Search across column names and values. Returns column-level results with IDs — tells you where to look, not the data itself. Use before get_rows or describe_column. max_results capped at 50. Set semantic=true for embedding-based search (requires an embedding provider: JDATAMUNCH_EMBED_MODEL, GOOGLE_API_KEY, or OPENAI_API_KEY).

Discover FK candidates between this dataset and other indexed datasets (B5). For each non-PK column in the source, scans up to 20 other datasets' PK candidates and proposes joins where containment ≥ 95%. Sample-based (500 distinct values per source column).

Rank primary-key candidates for a dataset (B5). Each entry carries a confidence score plus the reasons that raised it (integer column, UUID format, no nulls, exact-count unique). Candidates are ranked from profile statistics, so confirm against the source system before treating one as the key.

Generate natural-language summaries for a dataset and all its columns. Works on already-indexed datasets — reads profiles from index.json, generates summaries, and writes them back. No re-parsing of source files. Summaries are also auto-generated during index_local.

Inspect, set, or reset the weight vector search_data uses to rank columns (name/value/type match weights plus the BM25 and semantic blend scales). Omit all args to inspect the effective weights and their source. Pass set_weights (a {weight: number} object) to override, or reset=true to clear. Scope with dataset (per-dataset overrides win over the global default, which wins over built-ins). Honored by search_data at query time. Unlike jcodemunch/jdocmunch, weights are tuned explicitly here (no ranking ledger). Tunable: name_exact, name_substr, name_word, ai_summary_word, value_exact, value_subs…

Verify an indexed dataset's on-disk integrity. Runs SQLite PRAGMA integrity_check, cross-checks row count and column list against index.json, and verifies index.json content hash. Reports stale-lock state from interrupted index_local runs. Returns overall_status: 'ok' | 'warning' | 'error'. Checks the integrity of the index, never the correctness of the underlying data.