Aggregate hiring records associated with occupational credential requirements. Candidate profiles and holder counts are excluded; the result describes demand evidence, not a proven shortage.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses important behavioral traits: candidate profiles and holder counts are excluded, and the result is only evidence, not proof of a shortage. This prevents misinterpretation. However, it does not describe the output format or any data limitations beyond the exclusions.
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 action, and contains no redundant wording. Every clause adds meaning: the exclusion and the caveat are essential for correct interpretation.
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 simplicity (zero params, no annotations, no output schema), the description covers the key aspects: what it does, what it excludes, and what the result means. It could be improved by specifying the output structure or return format, but the interpretive caveat compensates for the absence of an output schema.
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 zero parameters, so the input schema imposes no burden. The description adds context about what the aggregation includes (hiring records) and excludes (profiles, holder counts), which is more than enough for a no-parameter tool.
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 ('aggregate') and identifies a clear resource ('hiring records associated with occupational credential requirements'). It also adds scope by explicitly excluding candidate profiles and holder counts, and distinguishes itself from a shortage analysis by stating the result is 'demand evidence, not a proven shortage.' This is sufficient to differentiate from sibling tools like get_labour_demand.
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 implies a use case: summarizing demand evidence for credentialed occupations. However, it provides no explicit guidance on when to use this tool versus alternatives, nor does it name any sibling tools or exclusion scenarios. The exclusions mentioned are about data scope, not usage conditions.
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.
