Skip to main content
Glama
Sign In
csv-mcp

csv-mcp

by meysam-kazemi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_csv

Analyze CSV files by inspecting encoding, dialect, columns, types, malformed rows, and sample data.

Instructions

Inspect encoding, dialect, columns, types, malformed rows, and samples.

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>"
    },
    "name": "inspect_csv"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
optionsNo
sample_rowsNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden. While 'inspect' implies read-only, the description does not explicitly state that no modifications are made, nor does it disclose any other behavioral traits like file locking or resource usage.

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?

The description is concise at one line, listing key inspection areas without redundancy. However, it lacks any structural formatting (e.g., bullet points) that could improve readability for an AI agent.

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

Completeness4/5

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

Given that an output schema exists (context signal), the description does not need to explain return values. It adequately enumerates the inspection scope (encoding, dialect, columns, types, malformed rows, samples), making the tool's capabilities clear despite no parameter details.

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

Parameters2/5

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

Schema description coverage is 0% and the description adds no meaning to parameters. It does not mention 'path', 'options', or 'sample_rows'. The schema itself describes 'CsvOptions' sub-properties, but the description fails to explain top-level parameters or how they influence inspection.

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 verb 'inspect' and specifies the resource 'csv', listing concrete aspects (encoding, dialect, columns, types, malformed rows, and samples). This distinguishes it from sibling tools like 'read_csv' or 'preview_csv', which have different verbs and purposes.

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 explicit guidance on when to use this tool over alternatives. It does not mention context, prerequisites, or exclusion criteria relative to siblings like 'validate_csv' or 'summarize_csv'.

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

Deploy Server

Other Tools