graph_stats

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

No annotations are present, so the description carries the burden of disclosure. It accurately frames the tool as a non-mutating reporting operation, describes the Markdown return format, and specifies doc-comment coverage calculation. It does not discuss performance or prerequisites, but for a zero-parameter stats tool this is sufficient.

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 compact: a one-line purpose, a usage trigger, a useful coverage detail, and a precise return description. No filler or duplication exists.

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 tool with no annotations and an output schema present, the description covers purpose, output format, and when to call it. It could be slightly stronger by noting any required repository preparation or when to prefer sibling tools, but nothing essential is missing.

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 has no parameters and schema coverage is 100% (empty schema), so the baseline is 4. The description adds context about what the current repo's stats will include, which is helpful despite no parameter documentation being needed.

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 specific operation and output: returning node/edge counts by kind and relation, plus doc-comment coverage. This clearly distinguishes graph_stats from sibling tools like get_node, list_nodes, and type_hierarchy, which target different granularity.

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 explicitly says to call this tool first when engaging with a new Swift repo, giving clear contextual guidance. It does not name alternative tools or state when not to use it, so it stops short of a full when/when-not routing.

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