get_knowledge_base

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

With no annotations provided, the description carries the full transparency burden. It discloses that the operation is a retrieval ('Retrieve'), describes what is returned (reference docs plus book extracts from 4 UE5 textbooks), and sets an explicit usage expectation. It does not describe failure modes or size/performance caveats, but for a read-only knowledge lookup the behavior is well covered.

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 somewhat long but well structured: a clear first sentence, return semantics, parameter guidance, and an explicit rule block with useful mappings. The 'KB: see...' pointer and example block add minor redundancy, but the organization keeps the important information front-loaded.

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 a single parameter, no annotations, and an output schema that presumably defines the return shape, the description is nearly complete: it covers what the tool returns, how to choose the topic, and when it must be called. The only small gap is not explicitly differentiating it from search_knowledge_base, and the 'Example' topic call could mislead an agent into thinking 'Example' is a valid topic.

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?

The input schema provides only a bare string type with 0% description coverage, so the description must compensate. It does so with concrete example topics and a pointer to list_knowledge_base_topics() for the full option set. The lone example call uses topic='Example', which is slightly ambiguous, but the overall parameter guidance is strong.

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 a specific verb-resource pair: 'Retrieve knowledge base content for a given topic.' It then clarifies the exact return value ('full content of the matching reference doc(s) plus relevant book extracts') and positions itself as the 'primary anti-hallucination tool,' making its role unmistakable even among many sibling tools.

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 gives explicit when-to-use guidance with a MANDATORY RULE and concrete mappings from UE5 systems to topic values. It says to use list_knowledge_base_topics() for all options, but it does not explicitly contrast this with the sibling search_knowledge_base or state when not to use this tool, so it falls just short of a 5.

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