Research hub query (rationality / cognitive-psychology / philosophy)

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently describes the multi-stage pipeline: FTS5 search, relevance re-ranking, evidence tiering, and synthesis into a cited answer. It does not state read-only behavior or return details, but for a query-and-synthesize tool this is reasonably complete.

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 a single dense sentence that front-loads the core search behavior and then covers the re-ranking and synthesis steps. It is efficient, though the tail phrase 'grounds claims in mental models and cognitive-science findings over popular advice' is slightly awkward and could be tighter.

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?

There is no output schema, but the description explicitly indicates the answer form (cited, step-by-step) and the tiering scheme, which is enough for an agent to anticipate the result. All parameters are documented in the schema. An example or explicit return structure would improve completeness, but it is not essential for invoking the 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 description coverage is 100%, so the parameters are already documented in the input schema. The description adds context about FTS5 search and re-ranking that helps explain why topK and limit matter, but it does not add meaning beyond what the schema already conveys for most parameters.

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 names the resource (crawled research hub), the mechanism (FTS5 search re-ranked into VERIFIED/PROBABLE/REJECTED tiers), and the output (a cited, step-by-step synthesized answer). It does not explicitly contrast with a sibling like jev_research, but the specificity is strong enough that an agent can tell what the tool does.

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?

Usage context is implied: this is for research questions where claims should be grounded in mental models and cognitive-science findings rather than popular advice. However, there is no explicit when-to-use versus alternatives, and the description does not mention conditions that should route the agent to a different tool.

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