get_table_stats

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the returned data fields, which implies a read-only operation, but says nothing about privileges required to read stats views, what happens when a table was never analyzed (null/stale stats), or freshness/refresh considerations.

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?

A single front-loaded sentence with zero filler; the verb and resource lead and the field list follows efficiently.

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?

No output schema exists, but the description effectively enumerates the return fields, which covers the main informational need for this low-complexity read tool. Only the privilege/null-stats behavior is missing, a minor gap for a simple read.

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%: both 'table' and 'schema' are documented in the schema, including the 'omit rather than guess' guidance for schema. The description adds no parameter meaning beyond that, so the baseline of 3 applies.

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?

States a specific verb (Return) plus a precise resource (optimizer statistics for a table) and enumerates the exact fields returned (row count, blocks, sample size, last analyzed, degree, partitioned, compression). That field list cleanly separates it from siblings like get_column_stats, get_histogram, and count_rows.

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 says what it returns but never states when to use it versus alternatives such as get_column_stats, get_histogram, count_rows, or describe_table. There are no preconditions or exclusions, so the agent must infer usage entirely.

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