base_tableUsage

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

Annotations already show readOnlyHint=true and idempotentHint=true. The description adds behavioral context beyond annotations, specifying that the tool reports access frequency and per-user patterns, and that setting persist=True materializes a volatile table and returns its name. 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 relatively concise: two sentences for the main purpose and usage, followed by parameter lines. It is front-loaded with the key idea. Slight room for improvement in structuring the parameter details more cleanly, but overall efficient.

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 tool with 2 parameters and no output schema, the description covers the purpose, usage context, parameter behavior, and output (access frequency patterns, optionally a table name). It is complete for an agent to understand and invoke correctly.

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 baseline is 3. The description repeats the schema's parameter explanations (database_name, persist) without adding new semantic meaning. It does not improve over the schema's own descriptions.

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 reports 'access frequency and per-user query patterns for tables and views', which is a specific verb+resource. It distinguishes from sibling tool base_tableAffinity by explicitly stating the alternative use case.

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 explicit when-to-use guidance: 'Use when the user asks how often tables are accessed, which tables are most popular, or which users are running queries against a database.' It also includes an explicit alternative (base_tableAffinity) when not to use this tool.

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