get_entity_embedding

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

With no annotations, the description must fully disclose behavior. It indicates a read operation but fails to mention what the embedding looks like (e.g., vector dimensions, format), any side effects, or required permissions. The behavior is minimally described.

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, clear sentence with no superfluous words. It is front-loaded and efficiently communicates the core action.

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 complexity (no output schema, sibling tools with similar purposes), the description is too minimal. It lacks context about the embedding's content, typical use cases, or how it relates to other tools like semantic_search. The agent would likely need additional information to use it effectively.

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% (the parameter entity_name is described). The description adds no extra meaning beyond the schema, so it earns the baseline score of 3.

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 retrieves a vector embedding for a specific entity from the knowledge graph. It uses a specific verb and resource, and while there are sibling tools like semantic_search that also deal with embeddings, the purpose is distinct enough for an agent to understand the basic action.

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 like semantic_search or get_entity_history. There is no mention of prerequisites, context for usage, or when not to use it, leaving the agent without decision-making support.

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