query_context

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it returns structured JSON with matching entries, scores, and pagination, which is useful context beyond annotations. No contradictory information.

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. The first sentence immediately conveys the core purpose and output format. The second sentence provides usage differentiation. Every sentence is essential and information-dense.

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?

Despite no output schema, the description mentions the return format (structured JSON with entries, scores, pagination). All 6 parameters are documented in the schema with additional context in the description. The tool's behavior is well-specified given its complexity. Missing details about output structure could be improved but not critical.

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%; each parameter is described. The description adds value with usage examples, such as 'Use Klever-specific terms for best results (e.g. "storage mapper SingleValueMapper")' and suggests common type combinations like '["code_example", "documentation"] for learning'. This helps an agent invoke the tool effectively.

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 searches the Klever VM knowledge base for smart contract development context and returns structured JSON. It distinguishes from the sibling tool search_documentation by specifying this tool is for precise filtering by type or tags, while search_documentation is for human-readable answers.

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 explicitly tells when to use this tool versus search_documentation: 'Use this for precise filtering by type or tags; use search_documentation for human-readable "how do I..." answers.' This provides clear guidance on alternative tools.

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