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

No annotations are provided, so the description carries the burden. It discloses return contents (narrative answer, bullish/bearish ratio, source URLs) and grounding in social-search. It implies a read-only operation and does not hide side effects. However, it does not mention edge cases such as what happens if both params are provided or if neither is provided, which would improve transparency.

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?

Three sentences, front-loaded with what it is, then usage, then return value, then when-to-use. No fluff, no redundancy. Every sentence earns its place.

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 no annotations and no output schema, the description provides sufficient context to understand the tool's purpose, parameters, and output. It lacks mention of edge cases (both or neither param provided) and any rate limits or data freshness details, but these are not critical for an agent to make a selection decision. The description is nearly complete for a social sentiment tool.

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 has 0% description coverage, but the description fully compensates by explaining both parameters: ticker for a focused name read, query for free-form questions. It gives a concrete example (NVDA) and clearly distinguishes the two modes. This is more informative than standard schema descriptions.

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 what the tool does: 'Real-time X/Twitter sentiment narrative.' It distinguishes itself from the news article list and specifically frames its niche as capturing the 'vibe' via social media sentiment. This differentiates it from sibling tools like tengu_v3_intel_twitter or news sentiment tools.

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?

Provides explicit when-to-use guidance: 'Use when you want the *vibe* on a name right now... not the news article list.' Also explains how to choose between the two parameters: ticker for a focused fintwit read, query for a free-form social-media question. This is actionable and helps the agent decide.

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