create_statistics

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

With no annotations, the description discloses key behaviors: Data Warehouse only, rejection of SQL Analytics Endpoints, parameter interactions (fullscan vs sample_percent), and default fullscan behavior. It does not mention error cases but covers essential behavioral traits.

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 concise and well-structured: a single-line summary, constraint notes, then a clear parameter list. Every sentence adds unique value without redundancy.

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 description covers purpose, constraints, and parameter details comprehensively. While it might omit prerequisites or error handling, it provides sufficient context for an agent to use the tool correctly, especially given the output schema is present.

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 0%, so the description fully explains each parameter with meaningful details (e.g., example for qualified_table, rejection notes for item, defaults and overrides for fullscan/sample_percent). This adds significant value beyond the bare 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 clearly states it creates a single-column statistic on a table, with a specific verb-resource pair. It distinguishes from siblings like delete_statistics, update_statistics, list_statistics, and show_statistics by focusing on creation.

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 provides explicit context: only supported on Data Warehouses, SQL Analytics Endpoints are read-only, and single-column only due to Fabric limitation. It does not directly compare with alternative tools but the constraints are clear.

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