Search @imqueue documentation

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description reveals important behavioral traits: the ranking may be misleading for features covered by mutually exclusive packages, and the tool returns URLs that get_doc can read. The warning about preferring list_packages over default ranking is a nontrivial behavioral caveat that helps the agent act correctly. No contradiction with annotations.

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 longer than a simple two-sentence example, but every sentence contributes distinct information: scope, output elements, query types, use case, URL linkage, and package ambiguity handling. It is dense but well-structured, with the core action and output first, followed by practical caveats. No wasted sentences, though it could be tightened.

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's complexity, the presence of an output schema (so return values need not be detailed), and strong annotations, the description is highly complete. It covers search scope, result contents, query flexibility, package restriction, and an important edge case (mutually exclusive packages) with a concrete fallback. This is sufficient for an agent to select and invoke the tool correctly in most situations.

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 already has 100% coverage with detailed descriptions. The description adds extra semantic value by clarifying that the query can be 'a plain question or an exact symbol name' and by contextualizing the `package` parameter as the way to search within a package chosen via list_packages. This goes slightly beyond the schema descriptions, warranting a score above the baseline of 3.

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 precisely states the action ('Search the official @imqueue docs... and every exported symbol'), the resource scope, and the output ('returning the most relevant pages with their URLs'). It clearly distinguishes from sibling tools like get_doc (reads a page) and list_packages (lists packages), making the tool's role unambiguous.

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?

Explicit when-to-use guidance is provided: 'Answers how do I do X in @imqueue and confirms a signature before code is written against it.' It also gives a clear exclusion/alternative: for ambiguous queries like 'tracing' or 'database', it directs the agent to call list_packages first instead of relying on the top result, and to then use the `package` parameter to search within the chosen package. This fully addresses when to use vs. not use the tool.

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