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

With no annotations present, the description carries the full behavioral burden and succeeds: it discloses the paid cost, payment mechanism, unpaid-call error behavior, the provenance fields returned, and the intentional hedge that the tool does not assert proof. This is well beyond what the schema or title could convey.

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?

Each sentence adds information: core function, returned content, JSON/hedging, payment mechanics, unpaid behavior, and sibling routing. The most actionable facts are front-loaded, and there is no filler or repetition of the schema.

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?

For a paid, 7-parameter tool with no output schema, the description covers success output, failure/payment requirements, pricing, provenance, and alternatives. An agent has enough context to call it correctly and interpret the result envelope; the only small gaps are filter-parameter specifics already visible in the schema.

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?

The description maps conceptually to query and limit ('question or topic', '3-10'), and output details imply filters like open-access and retraction status. However, with 57% schema coverage, the description does not directly add meaning for from_year, to_year, open_access_only, or fields; it relies on self-explanatory names and schema constraints.

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 opens with a concrete verb and object: 'Find 3-10 scholarly candidates for a question or topic.' It goes beyond a terse label by listing the returned data and explicitly saying the tool does not claim a paper proves a statement. This also distinguishes it from the evidence_pack_preview and citation_report siblings.

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 is explicit: use this when the free evidence_pack_preview (at most 3 candidates) is not enough, and use citation_report to check references you already have. It also tells the agent what to expect on an unpaid call, which directly guides when and how to invoke the tool.

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