ads_telegram_ai_keyword_topic_list

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

No annotations are provided, so the description carries the full disclosure burden; it does contribute real value by declaring the operation read-only and clarifying that it never contacts Telegram (no external side effects or account risk). However, it says nothing about permissions, data freshness, pagination, or what the 'source counters' and recent-vs-previous comparison actually contain.

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?

A single front-loaded sentence that leads with the verb and resource, with the safety clause appended after the semicolon. No filler, though the safety note could be its own short clause for scannability.

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 annotations, no output schema, and 4 undocumented parameters, the description covers purpose and side-effect safety but leaves the agent without the parameter and result semantics needed to call it correctly. For a tool embedded among many lookalike 'list' siblings, that is insufficient.

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 description coverage is 0% across 4 parameters, so the description must compensate and largely fails to: limit, status (active/ignored), project_id, and window_days are never mentioned. The phrase 'recent-vs-previous activity' faintly gestures at the window concept but does not explain window_days or how status filtering changes results.

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 a specific verb ('List') and a precisely scoped resource ('aggregated demand-topic trends that group related keyword variants'), which is meaningfully distinct from sibling tools like ads_telegram_ai_keyword_list (individual keywords) and ads_telegram_ai_trend_snapshot_list. It stops short of naming those siblings explicitly, so differentiation is inferred rather than stated.

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?

Usage is only implied: 'aggregated demand-topic trends that group related keyword variants' suggests this is the topic-level rollup rather than the raw keyword list, but there is no explicit when-to-use, when-not-to-use, or named alternative. An agent must infer the routing decision from sibling names alone.

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