get_country_exposureGeographic concentration of the supply chain: which countries host the companies in a given segment (or across all 12 segments), ranked by company count. Answers 'how concentrated in Taiwan is advanced lithography' style questions. Country is recorded for all 615 companies.
| Name | Required | Description | Default |
|---|---|---|---|
| segment | No | Restrict to one taxonomy segment id (see get_segments). Omit for the whole dataset. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains the output is ranked by company count and adds a key completeness guarantee ('Country is recorded for all 615 companies'), which is valuable context. However, it does not specify sort order (ascending/descending) or output format, which would improve transparency further.
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 concise and front-loaded: the first sentence states the core functionality, the second gives a concrete example, and the third adds a data-completeness note. Every sentence earns its place with no redundant or filler content.
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 one optional parameter and no output schema, the description covers the essential aspects: what it measures, how results are ranked, and the data completeness guarantee. It could mention the exact output fields (country name and count) explicitly, but the current wording sufficiently implies them. Overall, it is complete enough for an agent to invoke and interpret results.
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 schema already fully documents the 'segment' parameter (restrict to one taxonomy segment, omit for whole dataset). The description adds the '12 segments' detail and reinforces that the tool can be used across all segments, but this does not significantly extend the schema's meaning. Baseline of 3 is appropriate given high schema coverage.
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's purpose: showing geographic concentration of the supply chain by country, ranked by company count, with optional segment filtering. The example question ('how concentrated in Taiwan is advanced lithography') and mention of 'all 12 segments' help distinguish it from sibling tools like list_countries or get_country_profile, though it lacks an explicit verb like 'lists' or 'returns'.
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 provides a concrete use case ('answers how concentrated in Taiwan is advanced lithography style questions') and explains the segment filtering behavior (given segment or across all 12 segments). It does not explicitly state when not to use it or mention alternatives, but the context is clear enough for an agent to select this tool for geographic concentration queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clearly distinct purpose: analyzing portfolio exposure, comparing companies, finding chokepoints, getting company details, country exposure, deals, segments, supply chain walk, and company search. No overlap in functionality.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., 'get_company', 'search_companies', 'find_chokepoints'). The convention is uniform and predictable.
9 tools is well-scoped for a specialized supply chain analysis server. Each tool serves a distinct query or analysis need without being too few or too many.
The tool set covers core operations: search, detail, comparison, portfolio analysis, chokepoint identification, country exposure, deals, and supply chain graph traversal. Minor gaps like historical trends or alerts exist but do not hinder primary use cases.