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pragprogrammer

YNAB MCP Server

get_transactions_by_account

Retrieve transactions for a YNAB account, excluding pending. Filter by date, uncategorized or unapproved status, and exclude specific fields.

Instructions

Get transactions for a specific account, excluding pending transactions.

Args: account_id: The account ID plan_id: The plan ID (use list_plans to find available IDs) since_date: Only return transactions on or after this date (YYYY-MM-DD) type: Filter by 'uncategorized' or 'unapproved' exclude_fields: Optional list of field names to exclude from each transaction. If omitted, the model's default exclude list is used (see FIELDS.md). Pass [] to return all fields. Pass a custom list to override the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNo
plan_idYes
account_idYes
since_dateNo
exclude_fieldsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that pending transactions are excluded, explains exclude_fields defaults and overrides, and notes the date format. It does not discuss pagination, rate limits, or auth, but for a read operation these are less critical. The presence of an output schema reduces the need to describe return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is well-structured with a clear purpose sentence followed by an args list. It is moderately concise; every sentence adds value (parameter descriptions, behavior hints). Minor verbosity could be trimmed, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (5 params, 2 required), 0% schema coverage, and presence of output schema, the description covers parameters and a key behavioral trait (pending exclusion). It omits pagination or limits, but these are acceptable gaps for a read tool with output schema. Adequate for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does so thoroughly: explains each parameter (account_id, plan_id with reference to list_plans, since_date format, type filter values, exclude_fields behavior with three clear options). This adds significant meaning beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Get transactions for a specific account, excluding pending transactions,' clearly stating the verb, resource, and a key behavioral filter. It distinguishes from sibling tools like list_transactions by specifying account-scoped retrieval and a pending exclusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides detailed parameter guidance (e.g., using list_plans to find plan_id, date format, filter options, exclude_fields behavior). It does not explicitly compare this tool to alternatives like get_transactions_by_category, but the context and parameter details imply appropriate use cases.

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

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