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openfinance_update_transaction_category

Corrects the category of one or more transactions (PATCH /transactions/:id). Pass items as an array of { transaction_id, category_id } — transaction_id comes from openfinance_list_transactions, category_id from openfinance_list_categories. This overrides Pluggy's automatic categorization AND teaches Pluggy: recategorizing a transaction automatically creates a Category Rule for this client (case-insensitive exact match on the transaction's data), so FUTURE similar transactions are categorized the same way — use this to fix miscategorized transactions and improve categorization accuracy going forward. Batch shape: returns { updated, results: [{ transaction_id, category, categoryId }], errors: [{ id, status, message }] } — per-item errors do not fail the whole batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

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  9. First observed

TDQS

A5/5.0
Behavior5/5

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

The description discloses important behavioral traits beyond the annotations: it overrides Pluggy's automatic categorization, automatically creates a Category Rule, and affects future similar transactions. It also explains batch behavior with per-item errors, which is not visible in the annotations. There is no contradiction with the 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?

The description is well-structured and information-dense but not bloated. Each sentence serves a purpose: purpose, parameter format, side effects/usage, and return shape. It is front-loaded with the core action and efficiently packs critical details.

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

Completeness5/5

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

The description is complete for a complex batch mutation tool with side effects. It covers the parameter structure, source of IDs, the side effect of creating rules, and the exact return shape, which is especially important given there is no output schema. It also references sibling tools for data sourcing, providing full context.

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?

The schema only defines 'items' as an array without any field descriptions (0% coverage). The description compensates fully by detailing the item structure as { transaction_id, category_id } and specifying that transaction_id comes from openfinance_list_transactions and category_id from openfinance_list_categories, adding essential semantics.

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's action: 'Corrects the category of one or more transactions' and identifies the HTTP method (PATCH /transactions/:id). It is unambiguous and distinguishes the tool from sibling list/sync tools.

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

Usage Guidelines5/5

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

The description provides explicit usage instructions: it tells users to pass an array of { transaction_id, category_id }, explains where to source each ID, and states when to use it ('use this to fix miscategorized transactions'). It also explains the side effect of teaching Pluggy for future transactions, giving clear context for appropriate usage.

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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