read_metadata

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

With no annotations, the description fully carries the burden. It discloses that only first rows are read for efficiency, provides output details (file info, per-column profile, warnings, suggestions), and implies read-only behavior. No contradictions.

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 concise with two paragraphs, front-loading the main purpose. Every sentence adds value, though it could be slightly tighter.

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, the description efficiently covers what's returned (file info, column profile with stats, warnings, suggested operations) and ties to sibling tools. It's complete for a simple profiling 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?

The sole parameter file_path is fully described in the schema. The description adds context about reading first rows but doesn't augment parameter meaning beyond the schema. Baseline 3 is appropriate.

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 profiles a data file, listing specific outputs (structure, types, quality warnings, suggested next steps) and explicitly positions it as the recommended first call for unknown datasets, distinguishing it from siblings like run_pandas_code.

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 explicitly recommends this as the first call when exploring an unknown dataset, and mentions efficiency (reads only first rows). While it doesn't specify when not to use it, the context is clear relative to sibling tools.

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