# data-cleaner-agent [Health: Active] **Category:** 💻 Developer Tools **Repository:** https://github.com/wesseltl/data-cleaner-agent **GitHub Stars:** 0 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/data-cleaner-agent ## Description Clean messy CSVs: an LLM picks the steps, tested code does the work. Data untouched by the model. ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `uvx` (confidence: high): ```json "mcpServers": { "data-cleaner-agent": { "command": "uvx", "args": ["agentic-csv-cleaner"] } } ``` ## Documentation & README # data-cleaner-agent Clean a messy CSV with an agentic workflow. An LLM decides *which* cleaning steps the data needs, and tested Python functions do the actual work. **The model plans the cleanup, it never touches your data values**, so nothing gets hallucinated or silently rewritten. Works offline out of the box (no API key). Can be driven by an LLM, and other AI agents can call it as an [MCP](https://modelcontextprotocol.io) tool. ## Before / after ``` Full Name , Country, Signup Date, Amount Paid full_name country signup_date amount_paid Alice ,Netherlands,2023-01-05,"€1.200,50" Alice Netherlands 2023-01-05 1200.5 Bob,nederland,05/01/2023,"$900" ─────▶ Bob Netherlands 2023-01-05 900.0 Alice ,NL,2023-01-05,"€1.200,50" Carol Germany 2023-02-10 1000.0 Carol , Germany ,2023-02-10,1000 Dan Belgium 2023-03-01 750.0 Dan,belgie,2023/03/01,"€ 750,00" ``` In one pass it fixed the headers, trimmed whitespace, parsed three different date formats to ISO, turned `€1.200,50` / `$900` / `€ 750,00` into numbers, standardized the country names, and dropped the duplicate Alice row. ## Install & run ```bash pip install agentic-csv-cleaner clean-csv messy.csv cleaned.csv # clean a file clean-csv messy.csv # or just print the result ``` No API key needed. The default planner is a set of offline heuristics. ## The idea An "agentic workflow" is just software with a few parts: ``` look at the data -> planner picks the steps -> run the steps -> report (rules, or an LLM) (tested code) ``` The decision that makes it safe to trust: > **The planner decides *which* tool runs on *which* column. The tools do the transformation.** > A language model is good at judgment ("this column looks like money") and bad at being a reliable > calculator. So the LLM only ever *picks* operations from a fixed set. It never reads a value and > writes back a "cleaned" one, which is where LLM data-cleaning usually goes wrong. ## Two planners, one loop | Planner | What it is | Needs | |---|---|---| | `RuleBasedPlanner` | Offline heuristics from a quick data profile. The default. | nothing | | `LLMPlanner` | Sends the profile + tool list to an LLM, gets back a JSON plan. | `pip install "agentic-csv-cleaner[llm]"` + `ANTHROPIC_API_KEY` | Both return the same list of steps, so the loop is identical. You swap the brain, not the plumbing. The log reports what each step actually did, including where a conversion could not produce a clean result (unparseable numbers/dates, unmapped categories), so a clean parse is distinguishable from a confident guess. ## Use it from other code or agents ```python from cleaner.api import clean_csv_text result = clean_csv_text(open("messy.csv").read()) print(result["cleaned_csv"]) print(result["steps"]) ``` ### As an MCP tool (Claude Desktop) Other AI agents can call the cleaner as a tool, so they clean a CSV properly instead of reformatting it token by token in the prompt. Three steps: **1. Install it** ```bash pip install "agentic-csv-cleaner[mcp]" ``` **2. Add it to your client's config** (Claude Desktop's config lives at `~/Library/Application Support/Claude/claude_desktop_config.json` on macOS, or `%APPDATA%\Claude\claude_desktop_config.json` on Windows): ```json { "mcpServers": { "csv-cleaner": { "command": "python", "args": ["-m", "cleaner.mcp_server"] } } } ``` **3. Restart Claude Desktop.** The agent now has a `clean_csv` tool that takes CSV text and returns the cleaned CSV plus a report of what it did. ### Use it with other MCP clients The same server works in any MCP client, only the config differs. The command is `python -m cleaner.mcp_server`. **Cursor** — `~/.cursor/mcp.json` (global) or `.cursor/mcp.json` (per project), hot-reloads: ```json { "mcpServers": { "csv-cleaner": { "command": "python", "args": ["-m", "cleaner.mcp_server"] } } } ``` **VS Code / GitHub Copilot** — `.vscode/mcp.json`. Note the different key (`servers`, not `mcpServers`) and the required `type`. Tools only run in Copilot **Agent mode**: ```json { "servers": { "csv-cleaner": { "type": "stdio", "command": "python", "args": ["-m", "cleaner.mcp_server"] } } } ``` **Windsurf** — `~/.codeium/windsurf/mcp_config.json` (create it if missing): ```json { "mcpServers": { "csv-cleaner": { "command": "python", "args": ["-m", "cleaner.mcp_server"] } } } ``` **Cline** — add it from the extension's MCP settings panel in VS Code. ## Understanding the report The tool doesn't just hand back tidy data, it tells you what each step actually did, including where it couldn't get a clean result, so you can tell a clean parse from a confident guess: ``` - coerce_numeric(amount): all 4 value(s) parsed cleanly - standardize_dates(signup): 1/3 value(s) could not be parsed, set to null - standardize_categorical(country): 1 value(s) not in the mapping, left unchanged: ['MARS'] ``` So instead of silently dropping a value or leaving a wrong category, it surfaces it, and you know exactly which cells to double-check. ## What's in the box `cleaner/tools.py` holds the transformations: `snake_case_headers`, `strip_whitespace`, `coerce_numeric`, `standardize_dates`, `standardize_categorical`, `drop_duplicate_rows`. Take `standardize_dates`. `2023-01-05` (year first, month in the middle) and `05/01/2023` (day first) need opposite parsing rules, and one global setting corrupts one or the other, so the tool decides per value. Mixed dates are genuinely ambiguous, and this handles them explicitly instead of guessing. ## Tests ```bash python -m unittest discover -s tests ``` ## License MIT.