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

Annotations already cover read-only, idempotent, non-destructive, open-world. The description adds material beyond that: offline execution, English-only input, a batch cap of 50/200 KB, and a per-call price ($0.005 USDC via x402 on Solana) plus an HTTPS equivalent. It does not disclose failure modes or what happens on over-limit input, keeping it below 5.

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?

Dense but front-loaded: model identity and output shape come first, then features, then batching, then pricing and endpoint. Every sentence carries information, though the trailing pricing/URL block slightly dilutes focus for an agent deciding whether to call it.

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, the description carries the return-value burden and does so concretely (compound, proportions, label, per-sentence breakdown, batch summary). Combined with annotations, an agent has enough to call it correctly; only error/limit behavior is unstated.

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 all three parameters and their limits are already documented in structured form. The description only restates the batch limit and the per-sentence behavior, adding negligible meaning beyond the schema — baseline 3 applies.

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?

Names the specific technique (VADER, Hutto & Gilbert 2014), the target domain (social media, reviews, short text), and the exact outputs (compound score -1..1, positive/neutral/negative proportions, label). It is immediately distinguishable from text-adjacent siblings such as text_stats or readability_extract.

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?

Implies usage through stated constraints (offline, English-only, short text, per-sentence or batch of up to 50) rather than explicit when-to-use/when-not guidance or named alternatives. An agent can infer suitability, but there is no exclusion statement for long-form documents or non-English text beyond the passing 'English' mention.

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