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

With no annotations, the description carries the full burden, and it does disclose the returned shape (counts, types, samples) and that it is a bounded summary rather than the whole graph. It does not explicitly state read-only semantics, auth requirements, or size limits, which is the remaining gap 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?

Two compact sentences, front-loading what the tool returns and following immediately with the escape hatch for full data. No redundant or filler text.

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 zero-parameter read tool with no output schema and no annotations, the description covers purpose, return contents, and the alternative for full data — everything an agent needs to call it correctly.

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?

The tool takes zero parameters, so there are no parameter semantics to explain and the baseline of 4 applies. The description correctly implies no filtering or scoping inputs are accepted.

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 states a specific verb ('Summarize') and resource ('the CWI catalog knowledge graph') and enumerates the summary contents (node/edge counts, types, sample nodes). This is clearly distinguishable from the artist/track/search siblings, which operate on catalog entities rather than graph structure.

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 selection rule for the full dataset: 'For the full graph, fetch GET /graph.json,' which tells the agent both when this summary suffices and where to go when it doesn't. It stops short of naming a sibling tool as an alternative or stating exclusions, so 4 rather than 5.

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