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

Annotations already cover read-only, idempotent, non-destructive, closed-world. Beyond that the description discloses meaningful content semantics and a real gotcha: districts.district_census_id is a 5-digit Census school-district code, not a county FIPS code, and demographics values do not overlap within a category with 'all' as total. It omits any mention of failure behavior for a bad name, but the enum already constrains that.

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?

A single front-loaded sentence with the purpose first and the workflow hint last; every clause carries information. It is dense and parenthesis-heavy, which costs some readability, but there is no filler.

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?

With one enum parameter, rich annotations, and an output schema, the description does enough: it explains what the returned schema contains and when to call it. The one gap is sibling differentiation against get_dimension, which matters in this crowded set of similarly named tools.

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% with an enum, so the baseline is 3. The description earns above baseline by attaching meaning to the values — e.g., schools requires district_code plus school_code together — which tells the agent something the enum alone 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: returns the schema of one shared lookup table, enumerating the six dimension names and what the response contains (key columns, attributes, link_keys, semantics). It is clear against describe_dataset, but it never distinguishes itself from the sibling get_dimension, which an agent could plausibly confuse with this tool.

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 trigger: 'Useful before writing a link_query join.' That tells the agent where this fits in a workflow. It does not, however, say when to prefer it over get_dimension or describe_dataset, so the alternative-selection guidance is incomplete.

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