Quickstart - https://github.com/jgravelle/jdatamunch-mcp/blob/main/QUICKSTART.md

Use it to make money, and Uncle J. gets a taste. Fair enough? details

Most AI agents explore tabular data the expensive way:

dump the whole file into the prompt → skim a million irrelevant rows → repeat.

That is not "a little inefficient." That is a token incinerator.

A 255 MB CSV file with 1 million rows costs 111 million tokens if you paste it raw. A single describe_dataset call answers the same orientation question in 3,849 tokens.

That is a 25,333× reduction — measured, not estimated, on a real 1M-row public dataset.

jDataMunch indexes the file once and lets agents retrieve only the exact data they need: column profiles, filtered rows, server-side aggregations, cross-dataset joins, and semantic search — with SQL precision.

Benchmark: LAPD crime records — 1,004,894 rows, 28 columns, 255 MB Baseline (raw file): 111,028,360 tokens  |  jDataMunch: ~3,849 tokens  |  25,333× reduction Methodology & harness · Full results

Index once. Query cheaply. Keep moving. Precision retrieval beats brute-force context.

Commercial licenses

jDataMunch-MCP is free for non-commercial use.

Commercial use requires a paid license.

jDataMunch-only licenses

• Builder — $39 — 1 developer
• Studio — $149 — up to 5 developers
• Platform — $799 — org-wide internal deployment

Want the full jMunch suite?

• Munch Trio Builder Bundle — $99
• Munch Trio Studio Bundle — $449
• Munch Trio Platform Bundle — $2,499

Stop paying your model to read the whole damn spreadsheet.

jDataMunch turns tabular data exploration into structured retrieval.

Instead of forcing an agent to load an entire CSV, scan millions of rows, and burn through context just to find the right column name, jDataMunch lets it navigate by what the data is and retrieve only what matters.

That means:

• 25,333× lower data-reading token usage on a 1M-row CSV (measured)
• less irrelevant context polluting the prompt
• faster dataset orientation — one call tells you everything about the schema
• accurate filtered queries — the agent asks for Hollywood assaults, it gets Hollywood assaults
• server-side aggregations — GROUP BY runs in SQLite, not inside the context window
• cross-dataset joins — combine two indexed files in a single SQL query
• semantic search — find columns by meaning, not just keyword match
• natural-language summaries — auto-generated descriptions of every column and dataset

It indexes your files once using a streaming parser and SQLite, stores column profiles and row data with proper type affinity, and retrieves exactly what the agent asked for instead of re-loading the entire file on every question.

Most agents still handle spreadsheets like someone who prints the entire internet before reading one article:

• paste the whole CSV to answer a narrow question
• re-load the same file repeatedly across tool calls
• consume column headers, empty cells, malformed rows, and irrelevant records
• burn context window on data that was never part of the question

jDataMunch fixes that by giving them a structured way to:

• describe a dataset's schema before touching any row data
• search for the specific column that holds the answer — by keyword or meaning
• retrieve only the rows that match the filter
• run aggregations server-side and get back a single result set
• join two datasets without loading either into the prompt
• orient themselves with samples before committing to a full query
• detect data-quality issues and column correlations automatically

Agents do not need bigger context windows.

They need better aim.

Understand a dataset's full schema — types, cardinality, null rates, value distributions, samples, and natural-language summaries — in a single sub-10ms call. No rows loaded.

Structured filters with 10 operators (eq, neq, gt, gte, lt, lte, contains, in, is_null, between). All parameterized SQL — no injection surface. Hard cap of 500 rows per call to protect context budgets.

GROUP BY with count, sum, avg, min, max, count_distinct, median. The computation stays in SQLite. One compact result set comes back instead of the data the model would aggregate itself.

search_data searches column names, value indexes, and AI summaries simultaneously. Ask for "weapon type" and get Weapon Used Cd back. Ask for "Hollywood" and get the column whose values contain it.

Semantic search (v0.8+): Enable semantic=true for embedding-based search. Queries like "where did the crime happen" match AREA NAME even without keyword overlap. Supports local embeddings (sentence-transformers), Gemini, or OpenAI as providers.

join_datasets combines two indexed datasets via SQL ATTACH DATABASE — inner, left, right, or cross joins. Column projection, per-side filters, ordering, and pagination. No data leaves SQLite.

get_correlations computes pairwise Pearson correlations between all numeric columns. Discover hidden relationships without manual exploration.

Every indexed dataset gets auto-generated summaries describing data shape, column types, ranges, cardinality, quality issues, and temporal spans — no external API calls needed.

get_data_hotspots ranks columns by composite risk: null rate, cardinality anomalies, and numeric outlier spread. get_schema_drift compares schema between two dataset versions and classifies changes as identical, additive, or breaking.

Every call reports tokens_saved and cost_avoided estimates. get_session_stats shows your cumulative savings across the session, with per-model cost breakdowns. Lifetime stats persist across sessions.

index_repo discovers and indexes data files directly from a GitHub repository — CSV, Excel, Parquet, and JSONL. Incremental by HEAD SHA. Supports private repos via GITHUB_TOKEN.

Indexes are stored at ~/.data-index/ by default. No cloud. No API keys required for core functionality.

• Token budget enforcement — every response is capped at a configurable token limit (default 8,000)
• Anti-loop detection — warns when an agent is paginating row-by-row in a tight loop
• Wide-table pagination — describe_dataset auto-paginates at 60 columns
• Hard caps on all parameters to prevent runaway queries

jDataMunch parses local CSV, Excel, Parquet, and JSONL files using a streaming, single-pass pipeline:

CSV/Excel/Parquet/JSONL file
  → Streaming parser (never loads full file into memory)
  → Column profiler (type inference, cardinality, min/max/mean/median, value indexes)
  → Natural-language summary generator (dataset + per-column descriptions)
  → SQLite writer (10,000-row batches, WAL mode, indexes on low-cardinality columns)
  → index.json (column profiles, stats, summaries, file hash for incremental detection)

When an agent queries:

describe_dataset  →  reads index.json in memory (< 10ms)
get_rows          →  parameterized SQL on data.sqlite (< 100ms on indexed columns)
aggregate         →  GROUP BY SQL on data.sqlite (< 200ms for simple group-by)
search_data       →  scans column profiles in memory (< 50ms)
join_datasets     →  ATTACH DATABASE + cross-store SQL (< 300ms)

No raw file is ever re-read after the initial index. The SQLite database serves all row-level queries.

For a 255 MB, 1,004,894-row CSV (measured on real data):

• Index time: ~43 seconds (one-time)
• describe_dataset: 35 ms, 3,849 tokens vs 111,028,360 tokens raw — 25,333×
• describe_column (single column deep-dive): 22–33 ms, ~600 tokens
• get_rows (indexed filter): < 100 ms
• Peak indexing memory: < 500 MB

pip install jdatamunch-mcp

For additional format support:

pip install "jdatamunch-mcp[excel]"       # Excel (.xlsx, .xls)
pip install "jdatamunch-mcp[parquet]"     # Parquet
pip install "jdatamunch-mcp[semantic]"    # Semantic search (local embeddings)
pip install "jdatamunch-mcp[all]"         # Everything

claude mcp add jdatamunch uvx jdatamunch-mcp

Restart Claude Code. Confirm with /mcp.

Add to your config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "jdatamunch": {
      "command": "uvx",
      "args": ["jdatamunch-mcp"]
    }
  }
}

Option A — CLI:

openclaw mcp set jdatamunch '{"command":"uvx","args":["jdatamunch-mcp"]}'

Option B — Edit ~/.openclaw/openclaw.json:

{
  "mcpServers": {
    "jdatamunch": {
      "command": "uvx",
      "args": ["jdatamunch-mcp"],
      "transport": "stdio"
    }
  }
}

Restart the gateway: openclaw gateway restart. Verify: openclaw mcp list.

Any MCP-compatible client accepts the same JSON block in its MCP config file.

index_local(path="/path/to/data.csv", name="my-dataset")
describe_dataset(dataset="my-dataset")
get_rows(dataset="my-dataset", filters=[{"column": "City", "op": "eq", "value": "Los Angeles"}], limit=10)

Installing jDataMunch makes the tools available. It does not guarantee the agent will stop pasting entire CSVs into prompts unless you tell it to use structured retrieval first.

Add this to your CLAUDE.md (global or project-level):

## Data Exploration Policy
Use jdatamunch-mcp for tabular data whenever available.
Always call describe_dataset first to understand the schema.
Use get_rows with filters rather than loading raw files.
Use aggregate for any group-by or summary questions.

Add the same policy to your agent's system prompt file (e.g. ~/.openclaw/agents/analyst.md), then reference it in ~/.openclaw/openclaw.json:

{
  "agents": {
    "named": {
      "analyst": {
        "systemPromptFile": "~/.openclaw/agents/analyst.md"
      }
    }
  }
}

Ask your agent: "How many tokens has jDataMunch saved me?"

The agent will call get_session_stats, which returns session and lifetime token savings with per-model cost breakdowns. Lifetime stats persist to ~/.data-index/session_stats.json across sessions.

get_rows, aggregate, and join_datasets accept structured filters:

{"column": "AREA NAME",    "op": "eq",      "value": "Hollywood"}
{"column": "Vict Age",     "op": "between", "value": [25, 35]}
{"column": "Crm Cd Desc",  "op": "contains","value": "ASSAULT"}
{"column": "Weapon Used Cd","op": "is_null","value": true}
{"column": "AREA",         "op": "in",      "value": [1, 2, 7]}

Multiple filters are ANDed. No raw SQL accepted — injection surface is zero.

The case where it doesn't help: you genuinely need every row for ML training or full exports. For that, read the file directly. For everything else — exploration, filtering, aggregation, orientation — structured retrieval wins every time.

Every column and row gets a stable ID:

{dataset}::{column_name}#column     →  "lapd-crime::AREA NAME#column"
{dataset}::row_{rowid}#row          →  "lapd-crime::row_4421#row"
{dataset}::{pk_col}={value}#row     →  "lapd-crime::DR_NO=211507896#row"

Pass column IDs directly to describe_column. Row IDs are returned in get_rows results.

All three implement jMRI — the open retrieval interface spec. Same response envelope, same token tracking, same telemetry pattern.

• analysts, finance, ops, and consultants working with large spreadsheets
• AI agents that answer questions about CSV, Excel, Parquet, or JSONL data
• anyone paying token costs to load files they query repeatedly
• teams that want structured, auditable data access instead of raw file dumps
• developers building data-aware agents who need a drop-in retrieval layer

Start with the QuickStart guide — zero to indexed in three steps.

Or if you prefer learning by doing: index a file, run describe_dataset, and look at what comes back.

That single call — 35 milliseconds, 3,849 tokens — tells you everything that would have cost you 111 million tokens to read raw.

That's the whole idea...