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csv-mcp-server

csv-mcp-server

by Navneet1710
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_csv_statistics

Computes statistical summary for a CSV file or a selected column, providing insights into data distribution and key metrics.

Instructions

Get statistical summary of a CSV file or specific column

Args: filename: Name of the CSV file column: Optional specific column name to analyze

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": {
      "filename": "<filename>"
    },
    "name": "get_csv_statistics"
  }
}

Input Schema

NameRequiredDescriptionDefault
filenameYes
columnNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations provided, and the description only states the function without disclosing behavior such as error handling, file existence requirements, or whether it modifies data. Minimal behavioral insight.

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

Conciseness4/5

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

Description is short and includes an Args section. Efficiently conveys the core purpose, though could be more structured with separate sentences for each parameter.

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?

Has an output schema, so return value details are not required, but the description does not mention what statistics are computed. Fails to contextualize among 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?

Schema has 0% description coverage, so the description adds basic semantics for filename and column parameters, but lacks details like path format or valid column names.

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

Purpose4/5

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

The description clearly states it provides a statistical summary of a CSV file or specific column. It distinguishes from siblings like get_csv_info by mentioning 'statistical summary', though it could specify what statistics are included.

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?

No guidance on when to use this tool versus alternatives like get_csv_info, query_csv, or read_csv. Does not specify prerequisites or scenarios where this tool is appropriate.

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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