Period-over-period percentage change for an indicator. Use for growth rates (YoY, QoQ, MoM).
| Name | Required | Description | Default |
|---|---|---|---|
| full | No | Return the full raw time series (heavy, many tokens). Default false → you get only the summary/stats, which is enough to ANSWER a question. Set true only when you must plot or export every point. | |
| time | No | ||
| entity | Yes | ||
| period | No | yoy | qoq | mom (default: yoy) | |
| indicator | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true, establishing safety. The description adds no new behavioral traits beyond the calculation nature. It does not contradict annotations, but also does not elaborate on side effects or prerequisites.
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 long, front-loaded with the core purpose, and contains no filler. Every sentence serves a clear informative purpose.
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, no output schema), the description is adequate but incomplete. It does not explain the return format, the role of the 'time' parameter, or what happens when data is missing. However, the 'full' parameter description in the schema compensates partially.
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 40%, and the tool description does not mention any parameters. Required parameters 'entity' and 'indicator' have no descriptions in either the schema or the description, leaving the agent to infer their meaning. The 'full' and 'period' parameters are documented in the schema, but the description adds no additional clarity.
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 'period-over-period percentage change' for an indicator, and specifies its use for growth rates (YoY, QoQ, MoM). This verb-resource pairing is specific and distinguishes it from sibling tools like 'calculate' or 'lag_analysis'.
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 explicit usage context ('Use for growth rates (YoY, QoQ, MoM)'), but does not mention when not to use it or compare with alternatives such as 'lag_analysis' or 'rolling_stats'.
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.
Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.
The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.
48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.
The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.
