One community’s GDP report by 2021 SGC census subdivision code (e.g. 4708004 for Swift Current): central estimate with the income/production range and every sector’s amount and percentage share, or the suppression reason where the data cannot support a point estimate. Codes come from get_economy_overview or /api/v1/economy.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | 7-digit 2021 SGC census subdivision code. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return structure (central estimate, range, sector shares, or suppression reason) and implies read-only behavior. However, it does not mention authentication, rate limits, or data freshness, which would be ideal.
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 purpose and details. No unnecessary words; every sentence adds value.
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?
Despite no output schema, the description explains the response format thoroughly (central estimate, range, sector shares, suppression reason). It also tells where to obtain valid codes. For a single-parameter read tool, this is complete.
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%, so baseline is 3. The description adds a concrete example (4708004 for Swift Current) and explains the format (7-digit 2021 SGC code), providing value beyond the schema.
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 retrieves a community's GDP report by census code, including central estimate, range, sector shares, or suppression reason. This distinguishes it from sibling tools like get_economy_overview, which provides an overview of codes.
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 indicates that codes come from get_economy_overview or an API endpoint, implying when to use this tool (after obtaining a specific code). It does not explicitly state when not to use it or name alternatives, but the context is clear enough.
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.
