intelligent_search

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

No annotations provided, so description carries the full burden. It reveals key behaviors: uses cached local catalog (fast, no rate limits), expands queries with synonyms and Serbian↔English translations, and suggests related datasets on no exact match. However, it does not mention potential staleness of cached data or behavior on empty query, which is minor.

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 concise with three sentences, front-loading the purpose and key features. Every sentence adds value: purpose, benefit (cached), behavioral traits, and usage guidance. No wasted 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 tool has 4 parameters, an output schema, and moderate complexity, the description covers core functionality, language support, and fallback behavior. It doesn't detail return values (output schema exists) or data freshness caveats, but is sufficient for most use cases.

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 100%, so baseline is 3. The description adds contextual value (e.g., cache, language support) but does not add new parameter-level semantics beyond what the schema already provides for each parameter.

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's purpose: 'Search datasets with semantic understanding and fallback suggestions (RECOMMENDED).' It specifies the verb (search), resource (datasets), and mechanism (semantic understanding, fallback). The RECOMMENDED tag distinguishes it from siblings like search_datasets.

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?

Explicitly says 'Prefer this over search_datasets() unless you need live API results or organization/format filters.' This provides clear when-to-use and when-not-to-use guidance, fulfilling the requirement for usage alternatives.

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