research_status

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

With no annotations, the description carries the burden of explaining behavior. It clearly states it returns job status, elapsed time, and progress, which gives the agent insight into the tool's semantics. It does not mention side effects, but as a read-only status check, the description adequately conveys that it is non-mutating. Some additional context (e.g., polling behavior) could be added but is not essential.

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 sentence that is front-loaded with the verb 'Check'. It packs the purpose, input source, and return values into one concise statement with no fluff.

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 simple single-parameter tool with no output schema, the description covers the purpose, input, and output sufficiently. It does not explain the full workflow or polling behavior, but the sibling tool names and the list of statuses make the operational context understandable. Minor gaps include explicit instructions on retry/wait behavior, but the description is adequate for an AI agent to use the tool correctly.

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 schema already provides a description for the single parameter, research_id, and the tool description repeats the same source ('from research_submit'). Thus, the description does not add meaning beyond the schema, so baseline 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 uses a specific verb 'Check' with a clear resource ('status of a submitted Brave Answers research job'). It also explains the input source (from research_submit) and the outputs, distinguishing it from submit and result tools.

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 indicates it is used after research_submit by referencing 'research_id from research_submit'. It also implies a workflow by listing status outcomes (running/completed/failed), but does not explicitly state when not to use it or name alternative tools. Still, the context is clear enough for an agent to know it should be used to monitor progress before fetching results.

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