nlp_ownthink

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds that it returns a list, dict, or pandas DataFrame, which is useful behavioral context, but doesn't disclose other behaviors like data freshness, pagination, or error handling.

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 concise, with a front-loaded title and a compact docstring for parameters and return. It avoids excessive prose and uses a standard structured format, though the URL line could be integrated more cleanly.

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?

With two parameters and no output schema, the description provides parameter docs and return types but lacks an example, explanation of indicator value semantics, or details on the returned data structure. It's minimally sufficient but has clear gaps for an agent to fully understand the tool's output and use.

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 0%, so the description must compensate. It explains 'word' is Chinese input and lists valid indicator values ('entity or desc or avp or tag'), adding meaning beyond the schema. It doesn't elaborate on what each indicator value returns, so it's not perfect, but it covers the key semantic information.

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 identifies the tool as a 'Knowledge Graph interface for financial research', which names the resource but lacks a specific verb like 'query' or 'lookup'. It is distinguishable from siblings by its unique 'knowledge graph' scope, but the purpose is vague about the actual operation performed.

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 explicit guidance on when to use this tool versus alternatives. The description only provides a general 'financial research' context without saying when this knowledge graph interface is preferable to other NLP or data tools, and no exclusions or alternatives are mentioned.

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