atlas_bug_oracle

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions git integration and confidence intervals, but omits output format, side effects (e.g., whether it modifies files), prerequisites, or how it behaves on large projects. This leaves significant behavioral gaps.

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 8-bullet capabilities list is verbose and redundant, with items like 'Predict where bugs are likely to occur' and 'Identify high-risk code changes' overlapping. The key differentiator is buried at the end, and the description could be condensed to a few key points.

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?

No output schema exists, so the description should explain what the tool returns and how to interpret scores. It does not describe the return format, example usage, or operational constraints, leaving agents without enough context to successfully invoke and use 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 descriptions cover all 6 parameters, giving a baseline of 3. The description adds related context like 'confidence intervals' and 'git history for churn analysis' which aligns with analysisDepth and includeGitHistory, but does not meaningfully enhance parameter understanding 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 clearly identifies the tool as a predictive bug detection and risk analysis engine, with explicit capabilities like 'Predict where bugs are likely to occur' and 'Calculate bug probability scores for each file/function'. It distinguishes itself from static analyzers but does not mention sibling tools like atlas_error_predictor, so differentiation is partial.

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 implies usage for predicting future bugs rather than static analysis, stating 'This is NOT a static analyzer - it predicts FUTURE bugs using ML-like heuristics.' However, it does not provide explicit when-to-use or alternatives, leaving the user to infer the intended context.

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