Metis · Data Analyst — Compare Profiles

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

No annotations are provided, so description carries full burden. It explains the comparison operation and return format but does not disclose whether the tool is read-only, potential side effects, error conditions, or performance implications. Basic behavioral traits are covered but not comprehensively.

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?

Description is concise and well-structured: first sentence summarizes purpose, then explains inputs, then outputs. Uses clear formatting with bullet points for return fields. No redundant information.

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 an output schema exists (not shown), description covers the key aspects: inputs, outputs, and usage. It could mention prerequisites (e.g., need to call profile_dataset first) but overall is adequate for a focused diff tool.

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 coverage is 0%, yet description adds full context: each parameter (before_profile, after_profile) is described as a JSON string from profile_dataset() and explains their roles. This compensates entirely for the lack of schema detail.

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?

Description clearly states the tool compares two dataset profiles and produces a side-by-side diff. It lists specific outputs like rows added/removed, columns added/removed, null count changes, type changes, and a human-readable summary. This distinguishes it from sibling tools such as profile_dataset.

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?

Explicitly instructs to pass JSON strings from profile_dataset() and describes the arguments and return value. While it doesn't explicitly state when not to use or list alternatives, the usage context is clear and specific enough for correct invocation.

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