compareSide-by-side metric comparison for two filtered groups of transactions (e.g. one category vs another, one month vs another). Scope defaults to all your completed statements; pass "scope" to narrow to specific accounts/products and/or a date range.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | ||
| metric | Yes | ||
| filterA | Yes | ||
| filterB | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| scope | Yes | ||
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the default scope is all completed statements, and the tool performs side-by-side metric comparison. No annotation contradiction exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, followed by a useful scope-default note and illustrative examples. Every sentence earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, nested objects, output schema present), the description covers the essential purpose, default scope, and example use cases. It does not explain return values, but the output schema handles that. It could be slightly more complete by naming sibling alternatives, but overall it is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by explaining that filterA/filterB are 'two filtered groups of transactions' and that 'metric' is the comparison metric. It also explains the 'scope' parameter's default behavior. However, schema description coverage is 0%, and the description does not elaborate on the individual filter fields or metric enum values, leaving a notable gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Side-by-side metric comparison for two filtered groups of transactions.' It includes concrete examples (category vs category, month vs month) and clearly distinguishes this from sibling tools like aggregate, group_by, and time_series by emphasizing two-group comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states when to use the tool: for comparing two filtered transaction groups. It also provides context on the default scope ('all your completed statements') and how to narrow it via 'scope'. However, it does not explicitly mention alternatives or when not to use this tool versus siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.