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

With no annotations provided, the description carries the full burden. It discloses that the call is blocking with ~10-30s latency, mentions the separate Brave Answers plan and ~$0.05 per call cost, and notes the output is AI-synthesized with citations. This goes beyond a generic description and gives meaningful operational context.

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 concise: three sentences that front-load the core purpose, then add practical details (blocking, billing, latency), and finally provide alternative tool guidance. Every sentence earns its place with no redundancy.

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?

Given the simplicity of the tool and 100% schema coverage, the description is quite complete for decision-making. It covers the use case, cost, latency, output nature, and alternative tool. It omits details about error handling or output format, but those are not critical for selecting 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 baseline is 3. The description does not add detail about the parameters themselves, but this is acceptable because the schema already fully documents each parameter. No additional semantic value is provided 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?

Description clearly states it provides an 'AI-synthesized, web-grounded answer with source citations' and distinguishes itself from the sibling research_submit by pointing to it for deep multi-iteration research. The verb and resource are specific and unambiguous.

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?

Explicitly says 'Use for a direct cited answer without doing your own synthesis' and names the alternative research_submit for deep research. This provides clear when-to-use and when-not-to-use guidance relative to siblings.

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

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

With no annotations, the description carries full burden. It discloses a key behavioral trait: for a running job, it returns how long it has been running instead of an error, and advises to call again later. This adds meaningful context beyond the schema.

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 two sentences, front-loaded with the primary purpose and a conditional note. Every word earns its place with no unnecessary elaboration.

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 one-parameter fetch tool with no output schema, the description covers both the completed and running states. It doesn't detail output format or error cases, but these are not essential given the tool's simplicity and the presence of sibling tools for status.

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 fully describes the sole parameter research_id as 'returned by research_submit,' and the description merely repeats 'research_id' without adding new semantic detail. With 100% schema coverage, 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 clearly states the action 'Fetch the result of a completed Brave Answers research job' with a specific resource (research result) and scope (completed job). It also differentiates from siblings by explaining the behavior when the job is still running, which is distinct from a status or submission tool.

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 provides clear usage timing: call to fetch results of a completed job, and if the job is still running, 'call again later.' It implicitly tells the agent to retry later rather than switching to another tool, though it does not explicitly name alternative tools like research_status.

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

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.

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

With no annotations provided, the description carries full responsibility. It discloses runtime (~90-300s), cost formula with concrete examples, asynchronous behavior (returns research_id immediately, runs in background), and the polling/fetching workflow. This is above and beyond what an annotation would typically provide, and gives the agent a realistic cost/benefit picture.

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 information-dense but each clause earns its place: purpose, duration, cost, usage constraint, async behavior, and follow-up steps. The cost details are slightly lengthy but valuable for decision-making. No filler or redundancy.

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?

Given the complexity (9 params, async, cost), the description covers the critical operational context: when to use, how long it takes, how much it costs, what it returns initially, and how to get the final result. It does not cover failure handling or cancellation, but with no output schema and strong sibling tool descriptions, this is a minor gap.

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%, so the baseline is 3. The description does not elaborate on individual parameters but does add cost-related context (e.g., 'full 20-query runs can exceed $1' connects to research_maximum_number_of_queries). This provides marginal added meaning 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 opens with a specific verb and resource: 'Submit a Brave Answers RESEARCH job'. It clearly distinguishes this from the sibling tools by naming research_status and research_result as follow-up steps, and contrasts with the quick 'answers' tool. The scope (multi-iteration web research) is explicit.

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 an explicit usage condition: 'only use when the user explicitly requests deep research'. It also tells the agent what happens after submission (poll with research_status, fetch with research_result), effectively guiding when to use this tool versus its siblings. This meets the 'explicit when/when-not/alternatives' bar.

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