slack_smart_search

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already convey safety. The description adds behavioral context: requires specific infrastructure (Vectorize + Workers AI), is hosted, and has a free call limit of 25/month shared across hosted tools. It does not contradict 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?

Four sentences with front-loaded purpose, followed by output details, then hosting requirement and pricing. No filler, but pricing could be secondary. Overall efficient.

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 explains return values (ranked results, relevance scores, etc.). It covers key constraints (hosted, pricing, limits). Lacks detail on result structure but adequate for a search tool.

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 descriptions cover all 4 parameters (100% coverage). The description adds extra meaning: clarifies 'query' as natural language or keyword, and provides specific bounds for 'days_back' (max 90 on Pro+, 7 on Free) not present in the schema.

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 'Semantic + lexical hybrid search across your indexed Slack history' with specific outputs (ranked results, relevance scores, channel/thread context, matched terms). It distinguishes from simpler search tools like slack_search_messages by emphasizing the hybrid nature and hosted requirement.

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 provides usage context: it is hosted-only and requires Vectorize + Workers AI. It also mentions pricing and call limits. However, it does not explicitly compare to sibling tool slack_search_messages or state when not to use it.

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