opentweet_analyze_best_times

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

Annotations declare idempotentHint=true and destructiveHint=false. The description adds valuable behavioral details: it pulls last ~100 tweets, computes a heatmap, has a 24-hour cooldown, and uses cheap API calls. No contradictions with 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?

Three sentences, each serving a clear purpose: first states the action, second details the method and cost, third specifies the cooldown. No extraneous words.

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 the single parameter, no output schema, and rich annotations, the description covers the tool's purpose, constraints (cooldown, API cost), and behavior. It lacks an explicit description of the output format (e.g., 'returns a heatmap object'), but this is not critical for a computation trigger 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 coverage is 100% for the single parameter x_account_id, with a description in the schema. The tool description does not add new semantic information beyond the schema's 'Analyze a specific connected X account (for multi-account users)'. Baseline of 3 is appropriate.

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 the tool triggers a fresh analysis of best posting times by pulling last ~100 tweets and computing an engagement-weighted heatmap. It uses a specific verb-resource combination and distinguishes itself from the sibling 'opentweet_get_best_times' by emphasizing the 'trigger fresh analysis' action.

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 provides context on when to use this tool: 'Uses owned-read X API calls (cheap under 2026 X pricing). Cooldown: once per 24 hours.' This implies it's cost-effective but rate-limited. However, it does not explicitly state when not to use it or suggest alternatives like opentweet_get_best_times for cached results.

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