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

Goes well beyond the annotations (readOnly/idempotent/non-destructive) by disclosing key behavioral traits: the data is delayed, privacy-thresholded, sourced from opted-in users, and that missing buckets are suppressed/unobserved rather than zero. It also clarifies the unit is rounded researcher-hours, not people or recommendations, which prevents serious misinterpretation of the payload. This is exactly the kind of context the description should add.

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 the core concept, then the data coverage list, then critical caveats. The middle enumeration is somewhat list-like but each item maps to a distinct data domain, so it 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?

Strong on data semantics and caveats, but with no output schema and only 33% param coverage, the description leaves limit and window behavior undefined. For a parameterized retrieval tool it is adequate but has clear gaps an agent would want filled.

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 only 33% (only symbol documented) and the description adds no meaning for limit or window. The window enum (now/today/week) is self-documenting, but the limit parameter and how window interacts with the described 'delayed' data are unexplained, so the description fails to compensate for the coverage gap.

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?

States the resource (research attention data from Gloom users) and enumerates the data domains covered: canonical listings, most researched tickers, abnormal attention, country/sector totals, price/volume context, news and history. Clear what it returns, though the verb is implicit and there's no explicit differentiation from sibling attention tools like apps.attention or social.ticker_posts.

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 when-to-use or when-not-to-use guidance. It never mentions alternatives such as apps.attention or social.ticker_posts, so an agent has no signal for choosing between overlapping attention/stream tools. Usage must be inferred entirely.

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