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

With no annotations, the description carries the full behavioral burden, and it does disclose the return content (definition, mechanism, examples, misconceptions, FAQ, sources) and the accepted input forms (Chinese, English, aliases). It does not state error behavior when a term is missing or the scope of covered terms.

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?

Front-loaded with a bracketed summary tag followed by one dense sentence that covers inputs and outputs. Efficient with no redundant sentences, though the bracketed label duplicates the body slightly.

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 single-parameter, read-only lookup with no output schema and no annotations, the description adequately explains what comes back and what inputs are accepted. It could be stronger by stating a fallback when a term is not found, but nothing essential is missing.

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 baseline is 3, but the description adds genuine meaning: it confirms the term may be given in Chinese, English, or an alias, matching the schema's id-style examples. It slightly exceeds the schema's own example list rather than merely repeating it.

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 (explain) and resource (AI/tech term) and enumerates concrete examples (MCP, RAG, GEO, x402) plus the return payload (definition, mechanism, examples, misconceptions, FAQ, sources). It does not explicitly distinguish itself from its glossary siblings such as search_glossary or compare_concepts, though the 'full explanation' framing implies the difference.

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: an agent can infer you call this when you need a plain-language explanation of a term. There is no explicit when/when-not statement, no naming of alternatives like search_glossary or compare_concepts, and no stated prerequisites.

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