db_describe_table

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

With no annotations, the description carries the full burden. It transparently states the output ('Returns full column details') and the tool's name implies a read-only operation. It does not explicitly mention side effects or error conditions, but for a metadata descriptor, this is reasonably transparent.

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 two sentences with no fluff. The first sentence states the function and returned fields; the second sentence explains the typical use case. Every word earns its place.

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 a single simple parameter, no output schema, and a clear return type. The description fully covers what the tool does and when to use it. Nothing important is missing for an agent to select and invoke it correctly.

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%, and the schema already defines the 'table' parameter with an example. The description adds little beyond stating 'specific table or view,' which does not significantly improve on the schema. Baseline of 3 is appropriate.

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 returns full column details (name, data type, nullable) for a specific table or view. It distinguishes itself from siblings like db_describe_schema (whole schema) and db_sample_data (data preview) by focusing on column metadata for one table.

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?

Provides explicit usage context: 'Use this when you need to know exact column names before writing a query.' This tells the agent a concrete scenario, though it does not explicitly name alternatives or exclusions. Still, the guidance is clear and actionable.

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