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a-i-m-a-n

CSV Insight & Cleaner MCP

inspect_csv

Load a dataset from a file or raw text to get row/column counts, data types, missing values, duplicates, and a sample for quick structural analysis.

Instructions

Load a dataset and return structural facts: row/column counts, dtypes, missing values, duplicates, and a sample.

Provide ONE of:

  • file_path: local path to a .csv, .tsv, or Excel (.xlsx/.xls) file.

  • csv_data: raw CSV/TSV text content (for when there's no file access).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csv_dataNo
filenameNo
file_pathNo
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry full disclosure burden. It mentions the outputs and the two input options, but entirely ignores the 'filename' parameter present in the schema. It also does not state what happens if both file_path and csv_data are provided or if none are provided, leaving significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences, with the main purpose stated first and input options listed clearly. It is front-loaded with the most important information and contains no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The purpose and input methods are clear, and the outputs are described. However, the unexplained 'filename' parameter and lack of edge-case handling (e.g., what occurs if both inputs are provided) make the description incomplete for a tool with multiple input modes and no annotations. It could also better differentiate from sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning to file_path (supported formats) and csv_data (raw text content), but provides no explanation for the 'filename' parameter. With schema descriptions entirely absent (0% coverage), this partial coverage is insufficient for complete parameter understanding, though it does help for two of the three parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Load a dataset and return structural facts' with specifics like row/column counts, dtypes, missing values, duplicates, and a sample. This is a specific verb-resource pairing and distinguishes from siblings like summarize_csv or preview_csv by emphasizing structural facts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit input guidance: 'Provide ONE of: file_path or csv_data' and explains when csv_data is appropriate ('when there's no file access'). It does not explicitly name alternatives or state when not to use this tool, but the context is clear enough for an agent to infer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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