get_schema

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

With no annotations provided, the description carries the behavioral burden. It discloses that the tool returns all tables and columns in a single call and emphasizes a performance trait. It does not explicitly state side effects, but 'get' and the output schema imply a read-only, well-defined response.

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 one tightly written sentence with no filler. It front-loads the core promise and immediately follows with the comparative value proposition, making it easy to scan.

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?

For a zero-parameter, bulk-read tool with an output schema present, the description covers everything an agent needs: scope, speed advantage, and differentiation from siblings. No required inputs or return-shape details are 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?

There are zero parameters, so the schema's 100% coverage vacuously documents all inputs. The description appropriately adds no parameter details because none are needed.

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 states a specific verb ('Get'), a specific resource ('ALL tables and their columns'), and a scope ('in one call'), which clearly distinguishes it from list_tables and describe_table. An agent can understand exactly what this tool does and how it differs from siblings.

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 explicitly positions this tool as faster than 'list_tables + describe_table', giving an agent a clear reason to prefer it over those siblings. It doesn't enumerate exclusion cases, but the zero-parameter, bulk-read nature makes the guidance sufficient.

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