jDataMunch MCP server
Tabular data retrieval. Index CSV/Excel, query rows, aggregate. 99%+ savings vs raw file reads.
82 stars114 downloads/wk
Reviews
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If you have run it, two minutes of your experience saves the next person an afternoon.
jDataMunch MCP tools (1)
write = sends, deletes, buys or postsRead from the package source without running it. The installed server may list more.
get_session_statsReturn cumulative token savings and cost avoided across all tool calls. Savings are modelled estimates, not per-call measurements.
Public scan report
scanner v0.1.10 · 2026-10-29 · same rubric, same numbers if you re-run it
- Code scan88 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 8 days ago15/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
How jDataMunch MCP compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 92/100
- higher than 95% of 48,255 servers
- Tools
- 1
- the typical server has 7
- Tools that change things
- 0%
- typical: 0%. Fewer write tools means less an agent can break
- GitHub stars
- 82
- more than 95% of 28,688 servers
- Downloads a week
- 114
- more than 66% of 12,191 servers with a package
What its tools let an agent do: 1 read
Install jDataMunch MCP in Claude Code, Cursor or VS Code
claude mcp add jdatamunch-mcp -- uvx jdatamunch-mcp
What the publisher says
From the jDataMunch MCP repository's README, as published. We do not edit it. Read it on GitHub
<!-- mcp-name: io.github.jgravelle/jdatamunch-mcp -->
jDataMunch MCP: Tabular Data Retrieval for AI Agents
jDataMunch is an MCP server for coding agents and analysts that answers questions about CSV, Excel, Parquet, and JSONL files without pasting the rows into the context window.
Index a dataset once, then retrieve column profiles, filtered rows, server-side aggregations, and cross-dataset joins — so a million-row file costs thousands of tokens instead of millions.
Install · Quickstart · Benchmarks · Commercial licensing
Free for personal use. Commercial use requires a paid license — terms below.
Why jDataMunch?
The problem. The default way an agent explores a spreadsheet is to paste it into the prompt. A 255 MB CSV with a million rows costs roughly 111 million tokens that way, and the model still has to reason through a million rows to answer "what columns are in here?"
The mechanism. jDataMunch profiles the file once — columns, types, cardinality, null rates, distributions — and stores that locally. Queries then run against the data, not against a copy of it in the prompt: filters, aggregations, and joins execute server-side and return only results.
The outcome. Orientation questions are answered from the profile. Row-level questions return matching rows. The raw file never enters the context window.
Evidence
Measured on a real public dataset, not estimated. Full harness and per-query results in benchmarks/.
Corpus: LAPD crime records — 1,004,894 rows, 28 columns, 255 MB
Baseline: 111,028,360 tokens to paste the raw file
describedataset: ~3,849 tokens — a 25,333× reduction**
Methodology & harness · Full results
What these numbers are and are not. The reduction is measured against pasting the complete file, which is what a naive agent does and what the token bill reflects. It is not measured against a competent human analyst who would never paste a 255 MB CSV. The multiple scales with file size: a 200-row spreadsheet has far less to save, and the honest figure there is closer to "no meaningful difference."
Typical latencies from the same run: describe_column on a single column, 22–33 ms and ~600 tokens.
Install
Requirements: Python 3.10+, any MCP-compatible client.
There is no install step. jdatamunch-mcp is a stdio MCP server with no CLI subcommands, so nothing needs to land on your PATH — point your client at uvx and it fetches and runs the server on demand.
Claude Code setup:
claude mcp add jdatamunch -- uvx jdatamunch-mcpNothing else. Don't have uv yet?
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
jDataMunch MCP: common questions
- Is jDataMunch MCP server safe?
- Yes, by our scan: it is graded A (92/100). Read the jDataMunch MCP safety report
- How do I install jDataMunch MCP?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does jDataMunch MCP need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is jDataMunch MCP maintained?
- The last commit was 14 days ago (2026-10-21). The latest release is v1.31.13.
- What can I use instead of jDataMunch MCP?
- Servers from other publishers that do the same job: Excel MCP Server.
- How does jDataMunch MCP compare with other MCP servers?
- safety score 92/100 (higher than 95% of 48,255 servers); tools 1 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.
Alternatives to jDataMunch MCP
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