analyze_model_size

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

With no annotations, the description carries the full burden and does well: 'Get' signals a read-only operation, and it discloses a real behavioral caveat by distinguishing exact table byte totals from best-effort labeling of individual objects. It does not mention cost/latency of scanning a large model or instance targeting requirements, which are the remaining gaps for an unannotated tool.

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?

Front-loaded with what is returned, then why it matters, in two tight sentences with no filler. Minor verbosity in the DAX Studio/Tabular Editor aside, but it earns its place by conveying provenance.

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?

An output schema exists, so return values needn't be spelled out, and the efficiency-review framing is complete. However, for a two-parameter tool with zero schema description coverage, leaving top_n and instance entirely unexplained is a meaningful omission that could cause a wrong or defaulted call.

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 mentions neither parameter. 'top_n' is genuinely ambiguous (top N tables? columns? storage objects?) and 'instance' is undocumented as to expected format or source. The description must compensate here and does not.

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?

States a specific verb and resource: retrieve VertiPaq storage byte totals per table plus the largest storage objects/columns. It also anchors the concept to known tooling (DAX Studio / Tabular Editor VertiPaq Analyzer), which tells an agent exactly what class of output to expect and separates it from generic sibling tools like get_model_summary or list_tables.

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?

It gives an explicit usage context ('Use this for efficiency review') and concrete interpretations of results (unused high-cardinality column, text column to split, excess history import). It stops short of naming an alternative sibling or a when-not-to-use condition, so it is strong but not complete routing guidance.

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