jsonb_analysis

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

With no annotations provided, the description carries the full burden for behavioral transparency. It indicates the tool analyzes and recommends, but it does not disclose whether it modifies anything, whether it requires special permissions, what side effects it might have (e.g., running heavy queries), or what the return value looks like. This level of detail is insufficient for an agent to fully understand the tool's behavior.

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 a single, concise sentence that immediately conveys the tool's core purpose. It contains no filler or redundant information, making it optimally structured and front-loaded.

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?

The tool has 3 optional parameters and no output schema, so the description needs to explain the return value and any prerequisites. It only states the high-level action, leaving out what the analysis produces (e.g., a list of recommended indexes, DDL statements) and whether the tool can operate without specifying a table. The description is too minimal to be considered complete.

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 0%, and the description does not mention any of the parameters (table, schema, database_name). While the parameter names are self-explanatory, the description fails to explain how these parameters affect the analysis or whether they are required. It does not compensate for the lack of schema documentation.

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's purpose: 'Analyze JSONB columns and recommend GIN indexes for better performance.' It uses a specific verb ('analyze'), specifies the resource ('JSONB columns'), and the outcome ('recommend GIN indexes'), which distinguishes it from sibling tools like list_indexes or index_dedup.

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 implies usage for analyzing JSONB columns to get GIN index recommendations, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention any alternatives. The context is clear but lacks exclusions or comparisons to other tools.

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