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

Annotations already cover read-only/idempotent/non-destructive, so the bar is lower. The description adds genuinely useful behavior: accent-insensitive matching, full-text depth into publications and transcripts, the exact return shape, and the id-to-fetch handoff.

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 core purpose and scoping, then the return contract and the sibling pointer. Dense but each sentence carries information; the parenthetical about the OpenAI entry point is slightly tangential but useful for routing.

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?

With an output schema present, the description needn't detail returns, yet it still names the result fields and the follow-up fetch step. Scope, matching behavior, and alternatives are all covered, so nothing an agent needs to call it correctly 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%, so all three parameters are already documented, including the 'concise queries beat full sentences' guidance. The description adds no further parameter syntax or semantics beyond what the schema provides, 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 verb and resource ('Search the Africa Multiple (AMIRA) research collection') and enumerates the covered corpora (items, bibliography, podcasts/videos, projects/sections). It clearly distinguishes itself from the richer sibling search_* tools by naming them.

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?

Names alternatives (search_research_items, find_related, list_*) and frames itself as the OpenAI/ChatGPT-compatible entry point, which implies when to pick it. It does not state an explicit when-not condition or routing rule beyond 'richer filtered tools are also available'.

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