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MCP YNAB Server

by klauern

set_preference

Idempotent

Persist a YNAB user preference by specifying its name and value. Valid fields include default_budget_id, category_cache_ttl_minutes, and confirm_before_post.

Instructions

Set a single preference and persist it to preferences.json.

``name`` must be one of the ``Preferences`` model fields:
``default_budget_id``, ``category_cache_ttl_minutes``, or
``confirm_before_post``. Bool values accept any of
``1/0/true/false/yes/no/on/off`` (case-insensitive). The empty string is
the documented way to clear ``default_budget_id`` (stored as ``None``);
on a non-Optional field it surfaces as a coercion error.

Note: changes to ``default_budget_id`` here are equivalent to calling
``set_preferred_budget_id``; both write the same field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
valueYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the idempotentHint annotation, the description reveals important runtime behavior: persistence to a JSON file, accepted boolean representations, empty-string clearing semantics for default_budget_id, and the coercion error that occurs on non-optional fields. These are exactly the behavioral details an agent needs and cannot infer from annotations alone.

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?

The description is well-structured and front-loaded: purpose first, then validation rules, then edge-case behavior, then a sibling-equivalence note. Every sentence contributes necessary information with no filler.

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 that there is an output schema to cover return values, the description provides enough context for correct invocation: permitted names, coercion rules, and clearing behavior. It is only slightly incomplete on the exact value representation for the numeric TTL preference.

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 0%, so the description carries the parameter burden and does so well: it defines valid values for name, documents boolean coercion formats for value, and explains the special empty-string case. A small gap remains because the expected format for category_cache_ttl_minutes (e.g., integer string) is not explicitly stated.

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 opens with a specific verb and resource: "Set a single preference and persist it to preferences.json." It then enumerates the exact valid field names, which makes the tool's scope unambiguous and distinguishes it from siblings like execute or get_preferences.

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 clearly implies this tool is for setting named preference fields one at a time, and adds an explicit equivalence note: changes to default_budget_id are the same as calling set_preferred_budget_id. It does not spell out when not to use the tool, but the sibling differentiation is strong enough for an agent to route correctly.

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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