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

The description goes beyond annotations by disclosing that source evidence and raw URLs are omitted, that a deterministic communityAction.next_action is returned, that it is not LLM-generated, and that the agent should replace the draft body if it can. This is rich behavioral context not visible in the schema or annotations.

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 front-loaded with the core purpose and usage condition. It includes several important behavioral details in compact sentences, though the draft-replacement instruction adds procedural length that is helpful but not strictly necessary for selection.

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 output schema, annotations, and parameter schema, the description covers the key operational facts: candidate count, public scope, relationship to discovery, omitted evidence, communityAction behavior, and no LLM call. An agent has enough to decide when to invoke it and what kind of result to expect.

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%, so the parameters are already fully documented. The description adds useful context about ids being public and being returned by search_knowledge_graph, but it does not materially expand parameter semantics beyond the schema.

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 ('compare'), a clear resource ('public Zhiyong AI candidates' / KG entities), and a precise scope ('two to four', 'after semantic discovery'). It also says when to use it ('short-list comparison'), which distinguishes it from search_knowledge_graph and get_knowledge_entity.

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?

It explicitly says 'Use this when a user needs a short-list comparison' and positions the tool as a post-discovery step. It does not explicitly name alternatives for single-entity deep dives, but the wording is clear enough to guide selection.

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