get_series_statsLatest value, previous, all-time min/max, mean, and change vs the previous period and vs a year ago — computed over the verified store, with the citation attached. The cheap way to answer "what is it now and how has it moved" without a full analysis run.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Source slug, e.g. 'fred' | |
| seriesId | Yes | Series id, e.g. 'DGS10' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals safety; the description adds beyond that by disclosing the data source ('verified store'), the presence of a citation, and the specific statistics computed. This is more context than typical, though no details on pagination or edge cases are provided.
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 concise sentences pack all necessary information: the statistics list, data source, citation, and usage guidance. No wasted words and the key facts are front-loaded.
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?
For a simple read-only tool with two parameters and no output schema, the description covers the return statistics, data scope, citation, and use case. It lacks an explicit return format, but the overall context is sufficient for an AI agent to select and invoke the tool successfully.
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 input schema already documents both parameters (source and seriesId) with examples, achieving 100% coverage. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.
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 summary statistics for a series, enumerating the exact statistics included (latest value, previous, min/max, mean, changes). It distinguishes itself from siblings by positioning as a lightweight alternative to a 'full analysis run', making its purpose specific and unambiguous.
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 explicit guidance on when to use this tool: 'the cheap way to answer what is it now and how has it moved without a full analysis run.' It implies alternatives like deep_analysis but doesn't name them explicitly, which prevents a 5 but still provides clear context.
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.
Each tool targets a distinct operation: Q&A, deep analysis, series metadata, observations, stats, catalog search, and monitor CRUD. The only potential overlap between answer and deep_analysis is explicitly disambiguated by use-case guidance, and delete_monitor is unambiguous by name even without a description.
Most tools follow a consistent verb_noun pattern (get_*, list_*, create_*, delete_*, search_*). However, 'answer' and 'deep_analysis' deviate from this pattern, making the naming convention slightly inconsistent.
With 11 tools, the server is well-scoped for a world data/statistics domain. It covers discovery, retrieval, analysis, and monitoring without unnecessary bloat, and each tool earns its place in the set.
The toolset provides a complete workflow: discover sources/datasets via list_sources/list_datasets/search_catalog, retrieve series/observations/stats via get_*, analyze via answer/deep_analysis, and manage monitors via create/list/delete_monitor. No critical operations are missing for the stated purpose.