list_tables

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

With no annotations, the description carries the full burden and does well: it discloses the exact result fields, the default scan scope (all non-system schemas when schema is omitted), and a performance caveat for large databases. It stops short of stating permission/auth requirements or ordering/pagination, but covers what an agent needs to invoke it safely.

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?

Front-loaded with the resource and return fields, then usage and the filtering tip. Every sentence carries information, though the final sentence partially restates the schema-omission behavior already stated earlier.

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, so the description must explain returns, which it does by listing the returned columns. Combined with usage routing and the filtering/performance caveat, nothing an agent needs to call this correctly is missing.

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 already 100%, so the baseline is 3, but the description adds real value beyond the schema by explaining where the schema value comes from (list_schemas output) and why to supply it (narrow scan range). Remaining parameters are only reinforced, not extended, so it lands at 4.

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+resource (list tables/views in GaussDB) and enumerates exactly what the result contains (name, type, estimated row count, comment). It clearly distinguishes itself from siblings by naming describe_table, execute_select and list_schemas by their distinct roles.

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?

Gives explicit when-to-use guidance: locate a table here, then use describe_table for structure or execute_select for sampling. It also names the alternative source of the schema value (list_schemas) and the large-database condition that favors passing schema.

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