list_tables

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

With no annotations, the description fully discloses behavior: returns tables with metadata, supports pagination, filtering via LIKE/NOT LIKE, and an include_detailed_columns parameter that reduces payload. 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 well-structured with Args and Returns sections, and every sentence adds value. It is somewhat lengthy but justified by the number of parameters and details.

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 the complexity (6 params, pagination, optional detailed columns) and the presence of an output schema, the description covers all aspects including return values. It is comprehensive.

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?

All six parameters are explained in detail in the Args section, including defaults and the effect of include_detailed_columns. The input schema has 0% description coverage, so the description compensates fully.

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 'List available ClickHouse tables in a database, including schema, comment, row count, and column count.' It distinguishes itself from siblings list_databases and run_query by specifying tables and their metadata.

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 clear context on what the tool does, but lacks explicit guidance on when to use it vs. the sibling tools (list_databases, run_query). No exclusions or alternative recommendations are mentioned.

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