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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context: the corpus is a fixed set of 13 structured papers, each with summary and key findings, and the tool centralizes otherwise scattered free PDFs. No contradiction with 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?

The description is three tight sentences: it front-loads the resource and scope, explains the value proposition, and closes with the optional filter. Every sentence earns its place with no filler.

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?

This is a simple one-parameter, read-only lookup with a rich output schema and strong annotations. The description covers scope, contents, queryability, and optional filtering, leaving no essential information missing for correct invocation.

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 coverage is 100% and the only parameter q is fully described in the input schema. The description merely repeats that there is an optional keyword filter without adding syntax, format, or behavior details, so the baseline of 3 is appropriate.

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 states exactly what the tool exposes: a fixed set of 13 institutional research papers on CBDCs, stablecoins, crypto regulation, and cross-border payments, each with a summary and key findings. It also communicates the queryable nature and optional keyword filter, making it clearly distinct from siblings like defi.concept_lookup.

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 gives clear context for when to use the tool: when an agent needs to query a curated set of institutional research papers that are otherwise scattered PDFs without a query layer. It does not explicitly name alternatives or exclusions, but the use case is well implied.

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