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by Alexnikolaev1
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Python
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analyze_csv

Analyze a numeric CSV column to detect anomalies and recurring patterns, reporting top matches with nearest-neighbor comparisons for explainable insights.

Instructions

Same as analyze_series, but loads the values from a numeric column of a CSV file.

Args: file_path: Absolute path to the CSV file. column: Name of the numeric column to analyze. subsequence_length: Pattern window length (see analyze_series). top_k: How many top anomalies/patterns to report.

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": {
      "file_path": "<file_path>",
      "column": "<column>",
      "subsequence_length": "<subsequence_length>"
    },
    "name": "analyze_csv"
  }
}

Input Schema

NameRequiredDescriptionDefault
top_kNo
columnYes
file_pathYes
subsequence_lengthYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does add behavioral context beyond the schema name: the values are loaded from CSV, the column must be numeric, and output is anomalies/patterns. But it omits read-only/side-effect profile, file-size or performance limits, and failure modes, and defers most behavior to the sibling.

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 differentiator is front-loaded in the first clause, followed by a compact Args list for the four parameters. Efficient and well-structured, with only minor redundancy in the top_k wording.

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?

An output schema exists, so return values need not be described, and parameters are covered. However, the actual analysis semantics are offloaded entirely to the sibling tool, leaving the description only minimally adequate for a tool whose behavior an agent must otherwise infer.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it largely does: all four parameters are given meaning (absolute path, numeric column name, pattern window length, number of top results). The only weak spot is subsequence_length, which is deferred to analyze_series rather than explained.

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

Purpose3/5

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

The description identifies the resource (a numeric column of a CSV file) and explicitly differentiates itself from the sibling analyze_series, but the core verb 'analyze' is only clarified indirectly through the top_k parameter ('top anomalies/patterns'). An agent cannot fully determine what the tool does without reading analyze_series, which is left as an external reference.

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

Usage Guidelines3/5

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

Implicit guidance exists: 'Same as analyze_series, but loads the values from a numeric column of a CSV file' tells the agent to pick this variant when the data source is a CSV column rather than an in-memory series. However, there is no explicit when-to-use/when-not statement, no prerequisites, and no mention of error conditions such as a non-numeric column.

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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