time_seriesCompute a time series by grouping transactions into week or month buckets and applying a metric — useful for trends. 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 | ||
| bucket | Yes | ||
| filter | No | ||
| metric | 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 context about default scope (all completed statements) and the ability to narrow, which is useful but not extensive. It does not contradict annotations.
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 core operation, and every clause adds value. No wasted words or redundancy.
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?
The tool has nested objects (scope and filter) and an output schema, so return format is covered. The description explains scope and the bucketing/metric concept, but omits the filter parameter entirely, which is a significant gap for a parameter that could affect data selection. Given the complexity, more detail on filter usage would be expected.
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. It explains bucket (week/month) and metric (applied per bucket) in plain terms, and describes scope (narrowing to accounts/products and date range). However, it completely omits the 'filter' parameter, leaving its purpose and structure unaddressed.
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 clearly states the tool computes a time series by grouping transactions into week or month buckets and applying a metric, with a clear verb ('Compute') and resource. It implies trend analysis, which differentiates it from generic aggregation, though it does not explicitly name sibling alternatives like group_by or aggregate.
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 provides context that it's useful for trends and explains the default scope (all completed statements) with the option to narrow via 'scope'. However, it does not explicitly state when to use this tool instead of siblings like group_by or aggregate, nor provide exclusions or alternative recommendations.
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.