get_entity_embedding

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

No annotations are present, so the description must convey behavioral traits. It only states the action without disclosing side effects, read-only nature, performance characteristics, or any constraints. The agent cannot deduce that this is a safe read operation without additional context.

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 sentence that is concise and front-loaded. However, it is somewhat terse and could benefit from slight expansion without losing conciseness.

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 simplicity (one parameter, no output schema), the description is minimally complete. However, it lacks mention of the return type (vector embedding) and does not clarify that it is a read-only operation, which would be helpful for agents.

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 coverage is 100% and the schema already describes the parameter 'entity_name'. The description adds only the phrase 'from your Memento MCP knowledge graph memory', which provides context but no additional semantic detail about the parameter itself.

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 it retrieves a vector embedding for a specific entity, using a specific verb ('Get') and resource. It mentions the knowledge graph context, but does not explicitly differentiate from siblings like 'get_entity_history' or 'semantic_search', which are distinct but also involve entities/embeddings.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it does not explain how this differs from 'semantic_search' which also uses embeddings, or when to prefer 'get_entity_embedding' over 'get_entity_history'.

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