analyze-csv

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'EDA and data science insights' but doesn't specify what the analysis entails, how results are returned (e.g., as text, files, or structured data), or any constraints like file size limits or processing time. For an AI-powered analysis tool with no annotations, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place, making it highly concise and well-structured for quick understanding.

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 complexity of an AI-powered analysis tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'analysis' outputs look like, how insights are delivered, or any behavioral traits. This is inadequate for a tool that likely produces varied results based on input parameters.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining 'EDA' or 'data science insights' in relation to parameters. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is provided.

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 tool's purpose: 'Analyze CSV file using Gemini's AI capabilities for EDA and data science insights.' It specifies the verb ('analyze'), resource ('CSV file'), and technology ('Gemini's AI capabilities'), distinguishing it from sibling tools like 'generate-thinking' and 'visualize-data' which likely serve different functions. However, it doesn't explicitly differentiate from siblings beyond the general domain.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, prerequisites, or scenarios where this tool is preferred over others. The only implied usage is for CSV analysis with AI, but this is too vague for effective tool selection.

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