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leafeye

lunchmoney-mcp-v2

by leafeye

bulk_update_transactions

Batch update up to 500 transactions at once by specifying IDs and fields to change. Categorize, mark as reviewed, or assign tags in one operation.

Instructions

Update multiple transactions at once (max 500). Each item needs an id plus the fields to change.

Common uses: batch-categorize transactions, bulk mark as reviewed, assign tags to multiple transactions. Fields per transaction: category_id, payee, notes, tag_ids, status, date, amount, currency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
transactionsYesArray of transactions to update, each with id + fields
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds the max 500 limit and the requirement that each item needs an id plus fields to change, but it does not disclose whether updates are atomic, how partial failures are handled, what the response looks like, or any permission requirements. This is a gap for a mutation tool.

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 concise and front-loaded: the first sentence states the core purpose, the second gives common uses, and the third lists fields. There is no fluff, and every sentence serves a clear purpose.

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

Completeness3/5

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

The description covers usage context and constraints (max 500, id required) but omits what the tool returns on success or failure, which is important given there is no output schema. It also lacks guidance on partial failure behavior. However, the input schema is thorough, and the common uses provide enough context for basic invocation.

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

Parameters3/5

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. The description primarily reiterates field names already defined in the schema, adding little semantic value beyond clarifying that each item needs an id plus fields to change, which the schema already implies via the required id property.

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 clearly states the tool updates multiple transactions at once with a max of 500, which is a specific verb, resource, and scope. It also lists common uses (batch-categorize, bulk mark reviewed, assign tags), distinguishing it from sibling tools like manage_transaction that likely handle single updates.

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 clear common use cases (batch-categorize, bulk mark reviewed, assign tags) implying when to use this tool. However, it does not explicitly mention when not to use it or name alternatives like manage_transaction for single updates, so it lacks explicit exclusions.

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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