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

With no annotations provided, the description carries the full behavioral disclosure burden. It details exactly which statistics are computed, states that the return is a formatted text report, and specifies error conditions for unreadable or empty files. It does not cover potential size limits or encoding concerns, but for a read-only analysis tool this is solid coverage.

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

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

The description is well-structured with a clear purpose statement, a bulleted list of statistics, an Args section, a Returns section, and an example. Every element contributes value and there is no redundancy, making it easy to scan.

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

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

Given the output schema exists (signal true), the description does not need to detail return fields. It covers input semantics, output format, error behavior, and an example, so an agent can invoke it correctly. The only notable gap is the lack of routeing relative to the sibling tool get_csv_summary.

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

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

The input schema has 0% coverage, so the description must define file_path. It does so clearly, explaining that it accepts absolute or relative paths and providing a concrete example. This is useful beyond the raw schema, though it stops short of mentioning supported file extensions or size constraints.

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

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

The description clearly states the verb ('analyze'), the resource ('CSV file'), and the outcome ('basic statistical information'). However, it does not mention the sibling tool get_csv_summary or explain how it differs, so an agent must infer the distinction.

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

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

The description includes an example invocation and describes the file_path argument, but gives no explicit guidance about when to use this tool versus get_csv_summary, nor any conditions or exclusions. The usage context is entirely implicit.

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

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

With no annotations, the description carries the burden of disclosure and does a good job by enumerating the exact return fields (rows, columns, column_names, etc.) and providing an example invocation. It does not cover error handling or file access behavior, but for a read-only summary tool the essential behavioral contract is clear.

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

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

The description is well-structured with Args, Returns, and Example sections, and the first sentence immediately conveys the purpose. A few phrases like 'this tool provides' are slightly redundant but not wasteful.

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

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

For a single-parameter tool, the definition provides parameter semantics, detailed return values, and an example, giving an agent enough to invoke it correctly. The main gap is not addressing how it relates to analyze_csv, which is a minor context shortfall.

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

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

The schema provides no description for file_path (0% coverage), so the description's clarification that it accepts 'absolute or relative path' adds essential meaning. The example also demonstrates realistic usage, though no additional constraints or formats are specified.

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

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

The description clearly states the action ('get a quick summary' of a CSV file) and the output format (JSON). It identifies the resource precisely, but does not differentiate from the sibling tool analyze_csv, so it falls just short of a 5.

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

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

Beyond the generic note that it is 'useful for programmatic processing', the description gives no guidance on when to use this tool versus analyze_csv or any exclusions. No alternatives or selection criteria are mentioned.

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