search_labels

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

With no annotations, the description carries the behavioral disclosure burden. It does reveal that matching is fuzzy and that the operation is a lookup, which is useful, but it doesn't mention ordering, uniqueness, case sensitivity, or whether the operation is safely read-only.

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?

Two succinct sentences: the first states the core behavior, the second gives practical usage guidance. Every phrase earns its place, and the most important information is front-loaded.

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, has a full input schema and output schema, and the description explains its role relative to query and get_knowledge_graph. It could better distinguish from label-oriented sibling tools, but this is a minor gap given the simplicity of the operation.

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 input schema already documents q and limit clearly. The description reinforces that matching is fuzzy but adds no new parameter-level details beyond what the schema provides.

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 opens with a concrete verb and resource ('Find entity labels... fuzzy-matches'), making the action and scope clear. It doesn't explicitly distinguish this tool from label-related siblings like get_graph_labels or check_entity_exists, though the fuzzy-match framing helps.

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 a clear usage context: locate topic-related entities before calling query or get_knowledge_graph. It doesn't state when not to use the tool or explicitly name alternative siblings, but the intended placement in a workflow is evident.

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