query_ckg

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

Though no annotations are provided, the description details key behaviors: case-insensitive matching, partial name resolution, depth limits (upstream 1-5, downstream fixed 2 hops), return format (Markdown report with two trees and taxonomy tag), and error handling (similar names listed if not found).

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 well-structured: a succinct summary, usage guidance, parameter details, and return format. Each sentence adds value without redundancy.

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 complexity (dependency subgraph), zero schema coverage, and no annotations, the description covers all essential aspects: purpose, parameters, behavior, return format, error handling, and differentiation from siblings. The output schema exists but the description adds necessary context.

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?

With 0% schema description coverage, the description fully compensates by providing clear meanings for each parameter: domain (exact name from list_domains), concept (case-insensitive partial match), and depth (default 3, range 1-5). It also explains how concept matching works.

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 returns the dependency subgraph around a concept, specifying both prerequisites and downstream concepts. It distinguishes itself from the sibling tool 'get_prerequisites' by noting that the latter is for only upstream chains.

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 advises when to use this tool ('local neighborhood of a concept') and when not to ('For ONLY the upstream prerequisite chain, use get_prerequisites instead'). It also suggests calling 'search_concepts' if the concept name is uncertain.

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