YNAB MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool as a list operation with summary data, which implies it's read-only and non-destructive. However, it lacks details on permissions, rate limits, pagination, or error conditions. The description adds some context about trend analysis but doesn't fully compensate for the missing 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.

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

The description is well-structured and front-loaded: the first sentence states the core purpose, followed by usage context, then parameter and return details. Every sentence adds value without redundancy. The bullet-point style for Args and Returns enhances readability without wasting space.

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 moderate complexity (1 parameter, no annotations, but with output schema), the description is reasonably complete. It covers purpose, usage, parameters, and return fields. The output schema exists, so the description doesn't need to explain return values in detail. However, it lacks behavioral details like error handling or data freshness, which would be helpful given the absence of annotations.

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 schema description coverage is 0%, so the description must compensate. It explains the single parameter (budget_id) by stating it's optional ('uses default if omitted'), which adds meaningful semantics beyond the schema's technical definition. However, it doesn't clarify what 'default' means or provide format examples, leaving some ambiguity.

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 with a specific verb ('List') and resource ('all months in a budget with summary data'), distinguishing it from siblings like get_month (single month) or get_budgets (budget metadata). It explicitly mentions the data fields included (income, spending, budgeting), making the scope 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 provides clear context for when to use this tool ('Useful for trend analysis — see how income, spending, and budgeting have changed over time'), which implicitly differentiates it from tools like get_month (single month detail) or get_transactions (transaction-level data). However, it does not explicitly state when NOT to use it or name specific alternatives, keeping it from a perfect score.

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