base_tableAffinity

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

Annotations declare readOnlyHint and idempotentHint, which the description does not contradict. The description adds context about the persist parameter materializing a volatile table, which clarifies a behavioral nuance beyond annotations.

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 extremely concise: two sentences for purpose and usage, then a bulleted parameter list. Every sentence is informative with no redundancy. Front-loaded with main purpose.

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?

While annotations and schema cover safety and parameters, the description lacks detail on the output format (e.g., list of table pairs, scores). For a tool with no output schema, more return value description would be helpful.

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 100% with descriptions for each parameter. The description's 'Arguments' section essentially repeats the schema descriptions without adding new meaning, so baseline score 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?

Description clearly states the tool identifies tables that co-occur in SQL queries, revealing JOIN relationships and affinity patterns. It differentiates from sibling base_tableUsage by specifying this tool is for co-occurrence, not access frequency.

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?

Explicit usage guidance: use when user asks about tables queried together, related tables, or common workflow tables. Provides clear alternative: use base_tableUsage for access frequency or query counts.

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