atlas_baseline_benchmark

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

No annotations provided, so the description carries full burden for behavioral disclosure. It does not mention side effects, permissions, or output format. However, as a benchmark, it's likely read-only and non-destructive, but lacks explicit statements. Score reflects adequate but not comprehensive transparency.

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, efficient sentence with a dash separating the main action from the usage purpose. It is front-loaded with the key information and contains no unnecessary words.

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 no output schema, the description should indicate what the tool returns. It mentions 'shows how much lift' but does not specify the format (e.g., numeric value, table). For a simple tool, this is a minor gap. Score reflects slight incompleteness.

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 tool has zero parameters, so schema coverage is trivially high (100%). Per guidelines, 0 params yields a baseline of 4. The description adds no param info, but none is needed.

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 tool's purpose: benchmarking Voidly ML models against naive baselines to show lift. It uses a specific verb ('benchmark') and resource ('Voidly ML models'), and distinguishes from sibling tools like 'atlas_competitive_benchmark' by emphasizing baseline comparison.

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 context: 'Use for honest model evaluation.' It implies when to use the tool but does not explicitly mention alternatives or when not to use it. Given the sibling list includes similar tools, explicit exclusion would improve, but the current guidance is sufficient.

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