dataloupe
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DATALOUPE_MCP_ROOT | No | Optional but recommended: confines all file access to this directory (symlink-escape-safe). | |
| DATALOUPE_MCP_READONLY | No | When set to 1 or true, the server refuses to write an artifact to a caller-specified out_path; visualize_data/diff_data still return an artifact in a fresh temp file. | false |
| DATALOUPE_MCP_MAX_BYTES | No | Per-file read cap in bytes. A file larger than this is refused before it is loaded. Set to 0 to disable. Defaults to 512 MiB. | 512 MiB |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_data_filesA | List tabular data files (CSV/TSV/JSON/NDJSON/Parquet/Excel) in a local directory, with sizes. |
| describe_dataA | Return the schema, row/column counts, and per-column statistics (type, nulls, unique, min/max/mean/median, top values) for a local data file. Token-efficient; reads a sample for very large files. |
| preview_dataB | Return the first N rows of a local data file as a Markdown table. |
| query_dataA | Run a read-only structured query over a local data file: filter (where), select columns, order_by, limit/offset, and group_by with aggregations (count/sum/avg/min/max). Returns a Markdown table. No SQL, no writes — the file is never modified. |
| sql_queryA | Run a read-only SQL SELECT over a local data file and get a Markdown table back. Supports: SELECT * | | aggregates COUNT/SUM/AVG/MIN/MAX, WHERE (=, !=, >, >=, <, <=, LIKE, IN) with AND, GROUP BY, ORDER BY [ASC|DESC], LIMIT, OFFSET. The table name in FROM is ignored (single-table). No writes, no arbitrary SQL execution — the query string is compiled to a safe read-only plan (no eval), and the file is never modified. |
| visualize_dataA | Turn a local data file (optionally after a query) into ONE self-contained, fully-offline, interactive HTML explorer file on disk (sortable/filterable table + column stats + charts). Returns the path. The user can open it in any browser; data never leaves the machine and the file has zero external requests. Use this to hand the user a shareable, explorable artifact instead of a plain text table. |
| diff_dataA | Compare two local data files (a git-style diff for data). Reports added/removed/changed/unchanged row counts (matched by --key when given), and optionally writes a self-contained offline HTML diff report. Both files stay local. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Most tools are clearly distinct: listing, describing, previewing, diffing, and visualizing each have obvious roles. However, query_data and sql_query overlap heavily in functionality and both return Markdown tables, so an agent may struggle to pick between them despite the SQL versus non-SQL distinction.
The dominant pattern is verb_data or verb_data_files: list_data_files, describe_data, preview_data, query_data, diff_data, visualize_data. sql_query breaks the pattern by being a noun phrase instead of a verb-first tool name, making it the one inconsistent outlier.
Seven tools is a well-scoped set for local data exploration and comparison. Each tool covers a distinct phase of working with tabular files, and none feel redundant enough to be cut entirely.
The surface covers the full exploration workflow: discover files, inspect schema/stats, preview rows, query/filter/aggregate, compare files, and produce a visual artifact. Minor gaps exist, such as no way to export query results to a plain file or combine multiple data files beyond diffing, but core workflows have no dead ends.