analyze_qbo_journal_anomalies

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

No annotations provided, so the description bears full burden. It discloses input format, row and size limits, output structure with severity levels, and that it only detects round numbers. This is comprehensive and sets proper expectations.

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?

Well-structured with the main action first, then details, usage, and limitations. It is slightly verbose with explanatory notes about rounding numbers, but each sentence contributes useful context for an AI agent.

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?

Despite no output schema, the description explains output format and severity thresholds. It covers input constraints, usage context, and limitations (Tier-0, only round numbers). For a tool with two parameters and no output schema, this is highly complete.

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%, yet the description adds clarity: defines csvText as raw CSV from a specific QBO export, and fileName as a label. It also mentions max rows and size not in schema. This adds meaningful value 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 states the tool scans a QBO Journal Entries CSV for round-number anomalies, specifying the type of anomaly and the context. It distinguishes from siblings like analyze_xero_journal_anomalies by explicitly mentioning QBO, and from the paid product by calling it a Tier-0 subset.

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?

Provides explicit usage examples ('when a user pastes QBO data and asks...'). It does not explicitly list alternatives among sibling tools, but the QBO-specific focus and description of what it detects imply when to use it. Slight room for improvement in naming when not to use.

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