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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context beyond that: no per-tool rate-limit sub-cap, cheap/fast path, and the specific response shape including period_start/end and total_time_periods. It also clarifies the related_tokens format. Nothing contradicts annotations.

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 structured with clear headings, a front-loaded purpose, an explicit comparison section, parameter list, response list, and nested story field list. Every sentence earns its place — no redundant fluff. It is longer than average but remains tightly organized and information-dense.

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?

For a 2-parameter tool with no output schema, the description fully covers the response structure, story subfields, related_tokens format, and comparison with the superset tool. This leaves no significant gaps for the agent to guess about return values or usage constraints.

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 the baseline is 3. The description adds practical value by providing concrete examples ('1h', '6h', '1d', '7d'), restating defaults, and explicitly defining the meaning of time_period ('how far back to look'). It doesn't introduce new parameter semantics, but the examples and defaults reinforce usability.

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 opens with 'Fetch current trending crypto stories with sentiment analysis' — a specific verb, resource, and clear scope. It immediately distinguishes the tool from `combined_trends_tool` by positioning it as the stories-only, cheap/fast alternative, which clarifies its unique role among siblings.

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 'When to use vs combined_trends_tool' section explicitly states when to prefer this tool (only stories needed), what the alternative provides (superset with words and summaries), its drawbacks (LLM-slowed, lower rate-limit), and an explicit exclusion: 'never call both for the same question.' This is model guidance.

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