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

No annotations are provided, so the description carries the burden. It clearly describes an analysis operation and enumerates the return fields (score, confidence, label, reasoning, per-entity sentiment). It does not explicitly state there are no side effects, but the read-only nature of a sentiment analyzer is strongly implied by the description and name.

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 concise sentence that front-loads the core action and resource, then immediately lists the output contract. Every part is informative and there is no redundancy.

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 no output schema present, the description correctly takes on the job of describing return values, and it covers score range, confidence, label range, reasoning, and per-entity output. The input parameters are fully covered by the schema, so an agent has enough context to call the tool correctly.

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%, so the baseline is 3. The description adds context about the four contexts and output meaning, but does not substantially extend what the schema already documents for the text and context parameters.

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 ('Analyze sentiment of text') and resource, and lists the supported contexts. It is unique among the sibling tools, so an agent can clearly distinguish it from other operations like summarize or tx_decode.

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 implies when to use the tool: whenever sentiment analysis is needed for text in crypto, finance, social media, or general contexts. It does not explicitly state when not to use it or name alternatives, but none of the siblings perform sentiment analysis, so the usage context is clear.

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