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 does this exceptionally well: it discloses that empty books are created on first write and the account is added as CASH OUT unless clearly revenue; that only current and future months are updatable, while past months with bank actuals are protected; and that an approval may be required on first use (write-tier). It also explains the mode and force parameters. All behavioral nuances are covered, exceeding the required 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?

Though the description is long, every sentence carries essential information. It is well-structured: the main purpose is stated first, then usage conditions, then critical behavioral notes, then approval context. The use of capitalization for the IMPORTANT note draws attention to a critical rule. There is no redundancy—each sentence contributes to the agent's ability to call the tool correctly.

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 tool's complexity (6 parameters, 3 required, mode/force semantics, creation behavior, protected months, approval flow), the description covers all aspects needed for correct invocation. It explains not just the parameters but also edge cases (empty books, past months), and provides the approval expectations. With no output schema, it doesn't need to explain return values; but the description even hints at what happens without proper mode (returns current values). It is complete for an agent to use the tool effectively.

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. However, the description adds substantial meaning beyond the schema: it explains that account_name can create a new account on first write and the default CASH OUT treatment; it explains the significance of mode (set vs add) and force (skipping confirmation) in the context of existing values; it clarifies the month format implicitly via the schema but adds context about fiscal_year. The description adds critical semantic guidance that is not derivable from the schema alone, fully compensating for any gaps.

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: 'Update projected values for specific accounts and months in the financial plan.' It uses a specific verb ('update'), a specific resource ('projected values' in the financial plan), and differentiates it from the sibling get_projections by focusing on modification. It also names the scope (specific accounts and months) and the context (financial plan). This is 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?

The description explicitly states when to use it: 'Use this when the user asks to change a projection, forecast, or budget number.' It goes further by providing critical usage rules: empty books created on first write, protection of past months, and the requirement to specify mode='add' or mode='set' with force=true for non-zero values. It also warns about the behavior without those modes (returns current values and asks for clarification). This is comprehensive and leaves no room for error.

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