get_row_count

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

With no annotations, the description carries the transparency burden. It clarifies that the tool returns an exact count for a single dataset, but it does not disclose whether this is a full scan or metadata lookup, nor any performance or failure characteristics.

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 compact: a purpose statement, a usage tip, and an Args section. Every sentence adds value, and the structure is easy to parse.

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 simple tool with one parameter and no output schema, the description covers purpose, context, and parameter semantics. It omits the exact return type, but given the name 'get_row_count' this is likely obvious; however, it could explicitly state that it returns a non-negative integer.

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?

Despite 0% schema coverage, the description explains the 'dataset' parameter as 'Exact table or view name, as returned by list_datasets,' giving agents a precise source and format. This adds substantial meaning to the schema.

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 uses a specific verb and object — 'Get the exact row count for one dataset' — which clearly defines its function. It also hints at its targeted use case, distinguishing it from siblings like run_sql_query and list_datasets.

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?

Explicitly instructs to use before run_sql_query on large tables to understand sampling scope. This provides clear contextual guidance and implies when not to use it (as a substitute for querying).

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