list_datasetsList the datasets (official releases/tables) available in one source, with coverage dates and links to the official table pages.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Source slug, e.g. 'fred' or 'statcan' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already indicates a safe read operation, and the description adds valuable context about the output (coverage dates, links to official pages). It does not contradict the annotation and clarifies the scope, though it omits details like pagination or authorization.
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 a single, front-loaded sentence that conveys the purpose, scope, and key output features without any wasted words. It is efficient and immediately informative.
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?
This is a simple list tool with one parameter, a readOnly annotation, and no output schema. The description sufficiently explains what is listed and how it is scoped, making it fully usable for an agent without further elaboration.
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 coverage is 100% and the only parameter 'source' is adequately described in the schema as a slug. The description reinforces that the source parameter narrows the listing, but it does not add new semantic detail beyond what the schema already provides. Baseline 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 uses a specific verb 'List' and clearly identifies the resource as 'datasets (official releases/tables) available in one source'. It distinguishes this tool from siblings like list_sources by scoping to datasets within a specific source and mentioning coverage dates and links.
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 phrase 'in one source' implies the tool requires a source parameter, but it does not explicitly state when to use this tool instead of search_catalog or list_sources. No exclusions or alternative suggestions are provided, leaving the usage context only implied.
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.