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

Annotations provide readOnlyHint=false and destructiveHint=false, but the description adds meaningful behavioral detail beyond that: editor permission is required, embedding/judge costs may be incurred, resolution refusal behavior, and the fact that embedding_model only affects new concept types. This gives the agent important execution-time expectations.

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?

Every sentence earns its place: core purpose, prerequisites/costs, best-practice probing, refusal behavior, parameter scoping, return-value pointer, and pointer to documentation. It is front-loaded with the main purpose and remains compact despite covering multiple operational considerations.

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, an output schema exists, and annotations cover mutability and destructiveness, the description is complete. It addresses prerequisites, costs, edge cases, alternatives, and even points to further documentation. Nothing critical for correct invocation is missing.

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 input schema already covers all parameters at 100%, so the baseline is 3. The description adds value by explaining alias_of semantics, the refusal behavior for conflicting aliases, and the special scoping of embedding_model to new types only. Most parameters remain schema-described, but the added context justifies a score above baseline.

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 verb and resource: 'Add an identity concept at zero usage, or add text as an alias using alias_of.' It clearly distinguishes the two modes of this tool and implicitly separates it from sibling tools like delete_semantic_concepts and update_concept_alias.

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 gives explicit when-to-use and when-not-to-use guidance: 'Probe first; if the text already resolves to an incumbent, offer that concept instead of blindly retrying,' and 'Aliases resolving to another concept are refused.' It also names the alternative for managing existing aliases: update_concept_alias.

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