Skip to main content
Glama

Méliuz MCP

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=false, destructiveHint=false), the description reveals a critical behavioral trait: recategorizing automatically creates a Category Rule that affects future transactions. This is valuable context not available 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.

Conciseness4/5

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

Description is well-structured and front-loaded with the core purpose. It uses clear sentences, but could be slightly more concise by removing minor redundancies (e.g., 'teaches Pluggy' phrasing is clear but adds length). Still very effective.

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?

For a mutation tool with no output schema, description provides complete context: input format, source of IDs, side effects (rule creation), and batch response structure (updated, results, errors). This covers all necessary aspects for an AI agent to invoke correctly.

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?

Schema coverage is 0%, so description fully compensates. It explains the 'items' array structure, each field's purpose and origin (transaction_id from openfinance_list_transactions, category_id from openfinance_list_categories), and the batch response format.

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 action: 'Corrects the category of one or more transactions' via PATCH /transactions/:id. It specifies the resource (transactions) and action (update category), and distinguishes from sibling tools like openfinance_list_transactions and openfinance_list_categories by explaining where input IDs come from.

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?

Provides explicit guidance on when to use: to fix miscategorized transactions and improve future categorization. It tells how to structure the 'items' array and where to source transaction_id and category_id. Does not explicitly state when not to use, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct operation or data type (accounts, transactions, bills, loans, investments, connections, etc.) with no ambiguity. Even closely related tools like openfinance_list_transactions and openfinance_list_transactions_by_item are clearly differentiated by scope and output format.

Naming Consistency3/5

The majority of tools follow the 'openfinance_' prefix for banking operations, but utility tools (authenticate, connect, marketplace, report_bug, show_version, toolkit_info) break this pattern, creating an inconsistent mix. However, the convention is still readable and the utilities are clearly distinct.

Tool Count4/5

25 tools is on the high side but well-justified by the breadth of Open Finance data types (accounts, transactions, credit cards, bills, loans, investments) and supporting operations (sync, status, search, updates). A few tools could potentially be merged, but overall the number is reasonable for the domain.

Completeness4/5

The tool surface covers the core Open Finance workflows: listing, reading details, syncing, updating categories, and checking provider status. Minor gaps exist (e.g., no tool to create or delete accounts/transactions), but these are external constraints. The set enables most user-facing financial queries and actions.