get_knowledge_stats

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

Annotations already indicate read-only and idempotent behavior, and the description adds context about the exact output structure (total count, type breakdowns, sample titles). It does not contradict annotations and provides additional behavioral detail beyond the safety hints, such as the nature of the returned sample entries.

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 two sentences, front-loaded with the core purpose and followed by specific output details. Every sentence adds useful information, with no fluff or redundancy. It is appropriately concise for a simple stats tool.

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?

Since there is no output schema, the description fully explains the return values: total count, breakdown by context type with examples, and a sample title per type. It also provides a use case ('before querying'). For a zero-parameter tool with no output schema, this is complete and sufficient.

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, so the input schema is empty. The description does not need to explain parameters, and per the rubric, 0 parameters earns a baseline of 4. It adds value by describing what the returned statistics include, which is relevant for understanding the tool's output.

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 uses a specific verb 'Get' and identifies the resource 'summary statistics of the Klever VM knowledge base', clearly distinguishing it from sibling tools like query_context or search_documentation. It also details what the tool returns (total entry count, counts by context type, sample entry titles), making its purpose unambiguous.

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 states 'Useful for understanding what knowledge is available before querying', providing clear context for when to use it. However, it does not explicitly state when not to use it or name alternative tools, so it lacks explicit exclusions but is still clear about placement in a workflow.

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