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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.

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The description goes beyond annotations by detailing the behavioral impact: overriding automatic categorization, teaching Pluggy by creating a Category Rule, and batch error handling (per-item errors do not fail the whole batch). Annotations only state non-read-only, non-destructive, etc., but the description adds critical context. No contradiction.

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 detailed and front-loaded with the main action. It is slightly long but all sentences are necessary. Could be tightened by combining related points, but no redundancy.

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?

Given the complexity (batch operation, side effects, error handling) and the lack of output schema, the description provides a complete picture: input requirements, output shape (updated, results with transaction_id/category/categoryId, errors), and references to sibling tools. No gaps.

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?

With 0% schema description coverage, the description fully compensates by explaining the 'items' parameter structure: it is an array of objects with transaction_id and category_id, and it tells the agent where to obtain these IDs (from openfinance_list_transactions and openfinance_list_categories). This adds meaning beyond the raw 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?

The description clearly states the verb 'Corrects' and the resource 'category of one or more transactions', referencing the HTTP method PATCH. It distinguishes itself from sibling tools by specifying the source of transaction_id and category_id (list_transactions and list_categories).

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 guidance on when to use the tool: to fix miscategorized transactions and improve future categorization. It explains the side effect of creating a Category Rule, which helps agents decide whether to use this tool vs. other tools. It also implies when not to use it (if you don't want to affect future categorizations).

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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TDQS

A3.9/5.0
Disambiguation3/5

Most openfinance_* tools are clearly separated by resource and action, and the long descriptions help, but several pairs can trip up an agent: authenticate/connect both deal with login/connection state, openfinance_list_transactions and openfinance_list_transactions_by_item sound nearly identical, and marketplace internally exposes report_bug/list_tools functions that also exist as top-level tools. This is more than a single ambiguous edge.

Naming Consistency3/5

The 19 openfinance_* tools follow a clean get_/list_/update_ pattern and are easy to navigate, but the platform-level tools break the convention: authenticate, connect, marketplace, toolkit_info, report_bug, and show_version mix bare verbs, nouns, and noun-noun compounds. The pattern is not chaotic, but it is definitely mixed.

Tool Count3/5

25 tools is on the heavy end of the borderline range and is a large working set for an agent. The openfinance tools are individually justified and support batching, but the extra platform/marketplace tools add scope and some redundancy with report_bug and toolkit_info.

Completeness5/5

For a bank-data aggregation server, the surface is complete: connector discovery, linking/reconnecting/disconnecting, account lists and details, balances, transactions, categorization, credit-card bills, investments, loans, sync status, and provider health are all covered. There are no obvious dead ends in the main workflows.