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

With no annotations, the description carries the full burden, and it does add real behavioral content: parallel fan-out across four sources, a four-layer verification pipeline (existence, recency, multi-source corroboration, allowlist), combined_score ranking, and provenance tagging. That is genuine value beyond the schema. It still omits result volume/limits, failure behavior when a source is down, and what 'provenance tagged' concretely looks like in the response.

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?

It is a single compact sentence with no filler, and the source list is front-loaded. However, it is compressed into jargon fragments ('4-layer verification', 'combined_score ranking') that trade clarity for density, and key facts such as what the output contains are dropped rather than prioritized.

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?

Four well-described parameters at 100% coverage and a modest read-only discovery operation mean the schema does most of the work, and there is no output schema requiring explanation. The description supplies the pipeline behavior an agent needs to decide to call it; only result shape and result-count expectations are missing, which is a minor gap at this complexity.

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 four parameters (topic, domains, hours_back, include_sources) are already documented, which sets the baseline at 3. The description's mention of 'allowlist' loosely echoes the domains parameter and 'recency' echoes hours_back, but it adds no syntax, precedence, or defaulting detail beyond 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 names concrete sources (GDELT, BBC RSS, HN Algolia, Reddit JSON) and the processing pipeline, so an agent can tell it aggregates and verifies news/social items rather than searching a single index. But the framing 'literature discovery factory' is misleading (these are news/social feeds, not literature), and it never says plainly what the tool returns. It also does not differentiate itself from the sibling tool 'research_pack', which appears to overlap in intent.

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?

There is no guidance on when to use this versus 'research_pack', 'reddit_search', 'hn_frontpage', or the paper-search siblings. No prerequisites, no when-not-to-use conditions, and no indication of what a good 'topic' input looks like are given.

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