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

Annotations already declare the full safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false). The description adds meaningful behavior beyond that: the mode switch between list vs. semantic search, the source kinds (files, links, pasted text), and that search returns a grounded answer plus snippets.

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-loads the resource definition, then clarifies the two modes and the intended use. Sentences are purposeful, though the parenthetical source enumeration is slightly verbose.

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?

An output schema exists, so return-value detail is not required. The description covers the dual-mode contract and intended usage well; only explicit alternative-tool routing 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 description coverage is 100% and the single query param's dual meaning (question vs. null for listing) is already documented in the schema. The description restates this same semantic without adding format or syntax detail, so the baseline 3 applies.

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 resource (the org's own uploaded 'Teach AI' knowledge sources) and its distinct dual behaviors: listing sources with no query, or semantically searching content with a query to return a grounded answer. This clearly separates it from siblings like search_ai_tester_voices or company_data, which operate on different corpora.

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?

Gives explicit context for use ('to ground ideation or to answer questions from the org's own material'). However, it does not name alternative tools or state when NOT to use it, so an agent must infer routing against similar search siblings on its own.

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