Extract Topics

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

No annotations are supplied, so the description carries the full behavioral burden. It reveals the underlying method (keyphrase extraction followed by clustering) but says nothing about determinism, cost, sensitivity to the threshold defaults, or what happens with short or duplicate inputs — all material for a clustering tool with five tuning knobs.

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 efficient sentence with the resource and method front-loaded and no filler. Brevity here is appropriate in form, though the sentence is too thin to do the necessary work.

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?

An output schema exists, so return values need not be described. However, with zero annotations and zero parameter documentation, the definition leaves the agent without enough information to call a five-parameter clustering tool correctly.

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 five parameters, and the description adds no parameter meaning at all. max_topics, min_topic_size, top_k_keyphrases, and similarity_threshold are entirely unexplained, leaving the agent to guess how to tune granularity.

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 and resource ('Discover high-level topics') plus the mechanism ('by clustering extracted keyphrases'), which separates it from siblings like extract_keyphrases (raw keyphrase extraction) and cluster_entities (entity clustering). It never names 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?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives. The phrase 'extracted keyphrases' faintly implies a pipeline position after extract_keyphrases, but the tool takes raw texts, so that implication is not actionable.

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