dataset_stats

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

With no annotations, the description carries the full burden. Calling it 'a quick orientation' implies a lightweight, non-mutating read, but it never states that it is read-only, requires no special permissions, or leaves the store unchanged — important for a tool sitting beside destructive siblings like delete_dataset and drop_graph.

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?

Two tight sentences with the substance front-loaded: what it measures first, when to reach for it second. No filler or restatement of the tool name.

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 two-parameter read tool with no annotations and no output schema, the description covers the returned dimensions (counts, graph distribution, dominant types/predicates) well. It stops short of stating safety/read-only behavior, which is the only notable omission.

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 100%, so the schema already explains both 'top' and 'dataset'. The phrase 'which types and predicates dominate' loosely maps to 'top', but the description adds no format, default, or behavior detail beyond the schema — baseline 3 applies.

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 names a concrete operation and its outputs — 'Size and shape of a dataset: triple counts, graphs, classes and properties' — which is specific enough to separate it from list_datasets, list_graphs, and sparql_query. An agent can tell what this returns without reading the schema.

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 a clear use context: 'A quick orientation for a store you have not seen before.' That is a real when-to-use signal, but no alternative is named (e.g. list_graphs for graph names only) and there are no explicit exclusions.

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