csv-mcp

by meysam-kazemi

Overview Schema Source Related Servers Discussions Admin

Python

Local

clean_csv

Clean CSV data by trimming whitespace, converting text case, and removing duplicate rows based on selected columns.

Instructions

Trim, change case, and deduplicate selected columns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>",
      "operations": "<operations>"
    },
    "name": "clean_csv"
  }
}

Input Schema

Name	Required	Default
`path`	Yes
`dry_run`	No
`options`	No
`overwrite`	No
`operations`	Yes
`output_file`	No
`column_types`	No
`spreadsheet_formula_policy`	No	escape

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jul 29, 2026

TDQS

C2.5/5.0

Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It only mentions operations but omits key behaviors like the ability to overwrite files (overwrite parameter), perform a dry run (dry_run), or specify an output file. It also doesn't mention that the tool can modify the original file or that it uses CSV parsing options.

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

Conciseness2/5

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

The description is a single sentence that is too brief. While concise, it omits critical context (like input/output files). It lacks structure and doesn't front-load the most important information (e.g., that it modifies a CSV file).

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

Completeness1/5

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

Given the complexity of the tool (8 parameters, nested objects, output schema), the description is extremely incomplete. It doesn't mention the output schema, parsing options, or the ability to specify column types and date formats. The agent cannot use the tool effectively based on this description alone.

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

Parameters1/5

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

The description adds no meaning to the parameters. With a schema coverage of 0%, the agent must rely on the description, but it only lists operations. It doesn't explain that 'path' is the input file, 'operations' is an array of objects, or the role of 'overwrite', 'dry_run', or 'output_file'. This is insufficient.

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's actions: trim, change case, and deduplicate selected columns. This directly distinguishes it from sibling tools like read_csv, query_csv, or merge_csv, which perform different operations.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool or when alternatives are better. It does not mention prerequisites, context, or exclusions. For example, it doesn't indicate that this tool modifies files (as opposed to read-only tools like inspect_csv) or that it should be used for data cleaning rather than data extraction.

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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