memory_unlinked_mentions

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

Annotations indicate readOnlyHint=true, consistent with the read-only nature of surfacing mentions. The description adds behavioral details: uses embeddings + entity graph, excludes existing links, and proposes latent connections. It does not contradict annotations and provides useful algorithmic context, though it does not cover return format or pagination.

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 three sentences long and front-loads the main purpose in the first sentence. It is well-structured and information-dense, though slightly verbose in the second sentence. Overall, it is concise and easy to parse.

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 has no output schema, the description explains the output type (auto 'similar_to' suggestions) and excludes existing links. It also suggests next steps, making it complete for a discovery tool. No major gaps are apparent.

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 covers all three parameters with descriptions (100% coverage). The description does not add significant new parameter semantics; it mentions the algorithm but not parameter-specific details. With high schema coverage, the baseline is 3, and the description does not elevate it.

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 surfaces unlinked mentions for a memory, which are semantically related but not explicitly linked. It distinguishes itself from typical text matching by using embeddings and entity graph, and the resource (memory) and verb (surface) are specific. The description differentiates it from siblings like memory_related or memory_query.

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 provides clear guidance on when to use the tool: to discover latent connections that the agent never made. It also suggests a workflow after discovery, such as using memory_extract_entities or storing a link, implying the tool is for discovery, not confirmation. It does not explicitly list when not to use it, but the context makes it clear.

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