veriscope_discover_concepts

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

No annotations are provided, so the description carries the full behavioral burden. It omits whether the operation is read-only or mutating, what the output looks like (no output schema exists), any rate or scope limits, and how many results are returned, leaving the agent with only a high-level purpose.

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 well-formed sentence with no redundancy. It front-loads the core action and scope efficiently.

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?

The tool is simple with two fully documented parameters, but the absence of an output schema means the description should ideally explain what is returned (e.g., concept names, URIs, confidence scores). It covers purpose but leaves the result shape unspecified.

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%, so the schema already documents both parameters with examples. The description adds no syntax, format, or constraint details beyond what the schema provides, making the baseline score of 3 appropriate.

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 states a specific transformative action: elevating colloquial questions or news metaphors into standardized canonical academic ontologies across 24 disciplines. It is clear what the tool does, but it does not explicitly distinguish itself from the sibling tools (inspect_topography, locate_evidence, verify_attestation, probe_radar).

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?

Usage is implied by the phrasing 'colloquial questions or news metaphors' and 'standardized canonical academic ontologies,' suggesting it should be used when mapping informal language to academic concepts. However, there is no explicit when-to-use guidance, no exclusions, and no named alternatives among the siblings.

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