KG Statistics

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the description's role is to add detail. It accurately lists the output fields, providing behavioral transparency about what the tool returns. No contradictions or missing critical behaviors.

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 a single sentence that front-loads the purpose and enumerates the outputs. Every word contributes value, with no redundancy. Ideal conciseness.

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?

Given the tool's low complexity (1 optional parameter, annotations present, output schema exists), the description fully covers what an agent needs: it names the tool's function and the specific statistics returned. No gaps.

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 sole parameter 'response_format' is optional with default 'json' and enum values. Although the schema has no description (0% coverage), the parameter is self-explanatory and the tool description does not need to elaborate. The baseline for 0 parameters is 4, and this single trivial parameter justifies a 4.

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 the tool retrieves knowledge graph statistics and lists specific counts (total entity count, total triple count, active triple count, breakdown by predicate type). This specific verb-resource pairing effectively distinguishes it from sibling kg_* tools that modify or query the graph.

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 implies usage for obtaining summary statistics. While no explicit when-not-to-use or alternatives are given, the narrow purpose and sibling tool names (e.g., kg_query, kg_add_entity) provide sufficient context for when to use this tool.

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