dathere/data-describe

Data Describe

Generate AI-powered documentation for a tabular data file using describegpt. Produces a Data Dictionary (column labels, descriptions, types), a natural-language Description of the dataset, and semantic Tags — all via the connected LLM (no API key needed in MCP mode).

Cowork note: If relative paths don't resolve, call mcp__qsv__qsv_get_working_dir and mcp__qsv__qsv_set_working_dir to sync the working directory.

Steps

1. Index: Run mcp__qsv__qsv_index on the file for fast random access.

2. Profile: Run mcp__qsv__qsv_stats with cardinality: true, stats_jsonl: true to generate the stats cache. describegpt reads this cache for column metadata, so it must exist first.

3. Describe: Run mcp__qsv__qsv_describegpt with the requested options (recommend all: true for comprehensive output). At least one inference option (dictionary, description, tags, or all) is required. Output defaults to <filestem>.describegpt.md.

4. Present: Display the generated Data Dictionary table, Description, and Tags to the user.

Options

| Option | Effect | |--------|--------| | --all (recommended) | Generate Dictionary + Description + Tags in one pass | | --dictionary | Data Dictionary only — column labels, descriptions, types | | --description | Natural-language dataset Description only | | --tags | Semantic Tags only | | --format | Output format: Markdown (default), JSON, TSV, TOON | | --language | Generate output in a non-English language (e.g. Spanish, French) | | --addl-cols-list | Enrich the dictionary with extra columns (e.g. "everything", "moar!") | | --tag-vocab | Constrain tags to a controlled vocabulary (comma-separated) | | --num-tags | Number of tags to generate (default: 5) | | --num-examples | Number of example values per column in the dictionary | | --enum-threshold | Max cardinality to treat a column as an enum in the dictionary |

Notes

• No API key needed in MCP mode — uses the connected LLM automatically via MCP sampling
• The stats cache must exist first for best results (step 2 creates it)
• Output defaults to <filestem>.describegpt.md
• For Excel/JSONL files, the MCP server auto-converts to CSV first
• Use --format JSON when you need machine-readable output for downstream processing
• Use --language to generate documentation in the user's preferred language