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oliverames

YNAB MCP Server

by oliverames

get_transactions

Read-only

Retrieve YNAB transactions with filters for account, category, payee, month, or status (unapproved/uncategorized). Use the raw import payee name to disambiguate truncated payee names.

Instructions

Get transactions with optional filters. Use type='unapproved' or type='uncategorized' to filter. Optionally filter by account, category, payee, or month. Each returned transaction includes 'import_payee_name_original' — the raw merchant string from the bank import (e.g. 'AplPay LS ONION RIVEMONTPELIER VT') — which encodes processor flag, merchant name (often longer than the cleaned payee_name), and city+state. This is the primary disambiguation field when payee_name is truncated or ambiguous. Note: large date ranges (6+ months on a busy budget) can return 50KB+ of data; narrow with categoryId/payeeId/month filters when possible.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
budgetIdNoBudget ID (uses default if not provided)
sinceDateNoOnly return transactions on or after this date (YYYY-MM-DD)
typeNoFilter by approval/categorization status
accountIdNoFilter by account ID
categoryIdNoFilter by category ID
payeeIdNoFilter by payee ID
monthNoFilter by month (YYYY-MM-DD, first of month)
lastKnowledgeOfServerNoDelta request server knowledge. When provided, returns { transactions, server_knowledge }.
Behavior5/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context: potential for 50KB+ responses and the role of import_payee_name_original for disambiguation, which goes beyond annotations.

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

Conciseness5/5

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

Concise (~100 words), front-loads purpose, then filter examples, then a key performance note. Every sentence adds value with no repetition or fluff.

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?

For a query tool with 8 optional parameters and no output schema, the description covers filtering, return field details, and performance considerations. Could mention pagination but not critical given openWorldHint.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter. Description adds value by explaining practical usage of filters (type, month) and performance implications, though it does not deeply elaborate on parameter semantics beyond schema.

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?

Clearly states it gets transactions with optional filters, distinguishing it from get_transaction (single transaction). Provides specific filter examples (type, account, category, etc.) and mentions the import_payee_name_original field for disambiguation.

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?

Explicitly advises using type='unapproved' or 'uncategorized' and warns about large date ranges, suggesting narrowing with categoryId/payeeId/month. Does not explicitly mention alternatives but context is clear.

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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