top_nReturn the top N groups ranked by metric (descending), per-currency for monetary metrics. Scope defaults to all your completed statements; pass "scope" to narrow to specific accounts/products and/or a date range.
| Name | Required | Description | Default |
|---|---|---|---|
| n | Yes | ||
| scope | No | ||
| filter | No | ||
| metric | Yes | ||
| dimension | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| scope | Yes | ||
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true and destructiveHint=false already provided, the description adds useful behavioral context beyond annotations: the descending ranking order, per-currency handling for monetary metrics, and default scope semantics. It does not contradict any annotations and gives the agent meaningful operational expectations.
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?
The description is two sentences, front-loaded with the primary behavior, and contains no filler. Every phrase adds useful information about ranking, currency handling, or scope.
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 (5 parameters, nested scope object, filter object) and the availability of an output schema, the description covers the core idea and default behavior but omits guidance on the 'filter' parameter and how it interacts with 'scope'. It is adequate but leaves clear gaps for a complex tool.
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?
Schema description coverage is 0%, so the description must compensate, but it only partially explains 'scope' and vaguely references 'metric' and 'n'. Required parameters like 'dimension' and 'filter' are left unexplained, and the description does not add meaningful semantics beyond what the bare schema enum names already convey.
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 states a specific action ('Return the top N groups ranked by metric descending') and clarifies the per-currency behavior for monetary metrics. This clearly distinguishes it from sibling tools like aggregate or group_by, which focus on grouping rather than ranking by top N.
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 gives clear context on the default behavior ('all your completed statements') and explains when to use the 'scope' parameter to narrow results. It does not explicitly name alternatives or exclusions, but the usage context is strong enough for an agent to infer appropriate invocation.
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.