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Bankstatemently

by io.github.bankstatemently

Categorize Transactions

categorize_statement

Run AI transaction categorization on a previously processed document, then return its category mappings. Returns cached categories with no charge if this document was already categorized. Consumes credits (pooled per page, same rate as the categorize toggle on the website) the first time — free on every re-fetch after. Every response includes a "summary" field: use it as the single source of truth for what happened.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_idYesDocument ID (from convert_statement or list_statements)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
errorNo
statusYes
messageNo
summaryNo
documentIdNo
categoryMappingsNo

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / properties / document_id / description
      Added value: +"Document ID (from convert_statement or list_statements)"
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changed
    • removedInput schema / properties / document_id / description
      Removed value: -"Document ID (from convert_statement or list_statements)"
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses credit consumption, page-pooled pricing, caching behavior, and the always-present summary field. This goes well beyond the sparse annotations and aligns with readOnlyHint=false, since categorization incurs credits on first use.

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 front-loaded with the primary action and then adds cost/caching and response guidance. There is slight redundancy between 'Returns cached categories with no charge' and 'free on every re-fetch after,' but the paragraph is compact and focused.

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 single-parameter tool with an output schema and annotations, the description covers prerequisites, cost behavior, caching, and response interpretation ('summary' field). An agent has enough information to call it correctly without external context.

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?

There is only one parameter and the input schema already describes document_id with 100% coverage, including its source. The description's 'previously processed' reinforces the prerequisite without adding significant new semantic detail.

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 names a specific verb and resource ('Run AI transaction categorization on a previously processed document') and states the deliverable ('return its category mappings'). It clearly distinguishes this tool from listing, conversion, and comparison siblings by focusing on categorization of an already-processed document.

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 phrase 'previously processed document' and the parameter description 'from convert_statement or list_statements' give clear prerequisite context for when to call it. It does not explicitly name alternatives or exclude cases like raw/unprocessed documents, but the intended usage is evident.

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

A4/5.0
Disambiguation4/5

Core tools are cleanly separated by resource: statements, transactions, transfers, credits, and benchmark all have dedicated entry points. The five analytics tools (aggregate, group_by, compare, time_series, top_n) share metric/filter language, but their distinct output shapes and careful descriptions prevent major confusion, with only group_by vs. time_series/top_n occasionally overlapping.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern such as convert_statement, list_transactions, rate_statement, and dismiss_statement. The analytics tools (aggregate, compare, group_by, time_series, top_n) break that pattern, but they are still recognizable, consistently styled, and form a meaningful cluster.

Tool Count4/5

At 16 tools, this is slightly above the typical 3-15 well-scoped range, but the server covers a broad workflow: upload, conversion, retrieval, categorization, analytics, transfer matching, rating, credits, and benchmarking. Each tool maps to a distinct capability, so the count feels justified rather than bloated.

Completeness4/5

The tool surface covers the full statement lifecycle from upload and conversion through retrieval, categorization, analysis, rating, and dismissal, plus useful side capabilities like credits and benchmark evaluation. Minor gaps exist—no permanent deletion and no way to manually edit category mappings—but dismiss_statement and categorize_statement provide adequate workarounds.

Resources