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browse_compare

Compare raw LLM responses with evidence-backed answers to identify hallucinations and verify information accuracy.

Instructions

Compare a raw LLM answer (no sources) vs an evidence-backed answer. Shows the difference between hallucination-prone and grounded responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's function but lacks details on how the comparison is performed, what the output format looks like, whether it requires specific data inputs beyond the query, or any rate limits or error conditions. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded in a single sentence, efficiently stating the tool's purpose without unnecessary words. However, it could be more structured by explicitly separating the function from usage context, but it earns its place by being clear and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of comparing answers and the lack of annotations, output schema, and poor parameter documentation, the description is incomplete. It doesn't cover how the tool behaves, what inputs are needed beyond the query, or what results to expect, making it inadequate for effective agent use without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 1 parameter with 0% description coverage, and the tool description provides no information about the 'query' parameter. It doesn't explain what the query should contain, its format, or how it relates to the comparison process, failing to compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: comparing a raw LLM answer against an evidence-backed answer to show differences between hallucination-prone and grounded responses. It specifies the verb 'compare' and the resource 'answers', but doesn't differentiate from sibling tools like browse_answer or browse_search, which likely handle similar content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like browse_answer or browse_search. It mentions comparing two types of answers but doesn't specify prerequisites, context, or exclusions for usage, leaving the agent without clear selection criteria among siblings.

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

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