search_docs

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

With no annotations provided, the description fully carries behavioral disclosure. It reveals that related results are enabled by default (3-6 docs), that semantic matching uses existing embeddings with no extra API calls, that relevance thresholds and related-doc similarity adaptation occur, and that includes framework-aware connections. This goes far beyond a basic search description.

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 long but highly organized with clear sections, examples, and emoji signposts. It is front-loaded with purpose and strategy. Some redundancy exists between the 'ENHANCED BY DEFAULT' section and the includeRelated parameter description, so it is not perfectly concise, but the density is justified by the tool's feature richness.

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 no output schema, the description covers return semantics effectively: relevance score bands, related-document counts, and a concrete example of the related mode result structure. It also addresses the full workflow with siblings and parameter trade-offs, making it complete for an AI agent to select and invoke the 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 coverage is 100%, so the baseline is 3, but the tool description adds substantial value beyond the schema: it explains how to interpret relevance scores (60%+, 50-59%, etc.), gives query examples matching the format, and describes practical limit/minSimilarity settings. The includeRelated behavior and showCodePreview integration with get_code_examples are also elaborated beyond raw parameter docs.

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 'SEMANTIC SEARCH through 16,253 Apple documentation pages with intelligent relevance scoring,' which clearly names the verb, resource, and unique value. It distinguishes from siblings by explicitly positioning search_docs for exploration versus get_doc for detailed analysis and get_code_examples for code extraction.

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?

Provides a detailed SEARCH STRATEGY with broad-to-narrow iteration, specific limit recommendations, query examples with good and bad patterns, and explicit alternatives (get_doc, get_code_examples). The workflow line 'Use search_docs for exploration → get_doc for detailed analysis → repeat' makes when-to-use unmistakable.

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