Saskatchewan merchandise exports by NAPCS section from Statistics Canada. Station-level CGC rows are excluded pending commercial redistribution permission.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It does disclose a meaningful behavioral trait: 'Station-level CGC rows are excluded pending commercial redistribution permission.' This is genuinely useful caveat about data completeness. However, it doesn't disclose return format, data freshness, or whether the data is aggregated at provincial vs sub-provincial levels.
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 sentences, zero waste. The first sentence states the core purpose tersely, and the second provides a critical caveat about data exclusions. Every word earns its place.
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 zero-parameter tool with no output schema and no annotations, the description is reasonably complete about what data it returns. However, as a data retrieval tool it could disclose more about the granularity (time series? latest year only?), units (CAD?), and the nature of 'excludes station-level CGC rows' implication for interpretation. The exclusion note is good but leaves the reader wondering what CGC means and how complete the remaining data is.
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 tool has 0 parameters, so there are no parameters requiring semantic explanation. Per the rubric, 0 params = baseline 4. The description adds context about data origin (Statistics Canada) and scope (Saskatchewan, NAPCS section) which is helpful.
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 the tool provides Saskatchewan merchandise exports by NAPCS section from Statistics Canada, which is a clear verb+resource+scope. However, it doesn't distinguish itself from siblings like get_economy_overview or get_economic_signal, though the specific export focus is fairly unique among the sibling list.
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?
No guidance on when to use this tool vs alternatives. It doesn't mention when exports data would be preferred over other economic indicators, nor are any exclusions or alternative tools named. The implied context is general economic data retrieval but nothing explicit.
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.
Several tools are effectively duplicates or near-duplicates: get_emerging_signals is an explicit alias for list_economic_signals, resolve_entity and resolve_entities overlap heavily, and the markdown variants duplicate their non-markdown reports. Pairs like compare_communities/compare_municipalities and search_businesses/search_licensed_businesses also require reading long contracts to avoid misselection.
Tool names overwhelmingly follow a snake_case verb_noun pattern with sensible verbs like get_, list_, search_, and compare_. The main deviations are the backwards-compatible get_emerging_signals alias and prefix choices such as check_business_health vs get_business_health that obscure the underlying distinction.
70 tools is an extreme surface for any MCP server, far beyond the 25+ 'too many' threshold. The set is fragmented by format variants, aliases, and multiple overlapping lookup tools, making selection and maintenance costly.
The domain surface is broad: entity resolution, business health, labour, community economy, procurement, and governance evidence are all covered in depth. However, there are notable lifecycle gaps—no sandbox deletion, consent revocation, health-action cancellation, or actual exchange/connect/apply step—that leave agents with dead ends.
