SupplyGraph.AI

Search Region Candidates

search_region_candidates

Resolves natural-language country or region names (including aliases and abbreviations) against SupplyGraph’s internal geography registry and returns a list of standardized region names for downstream agent and MCP tool consumption.

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 1}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Country or region name in natural language, including aliases and abbreviations (e.g. 'China', 'USA', 'South Korea', 'Hong Kong', '中国', '美国'). The tool searches the internal geography registry and returns standardized matching region names for caller-side selection.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedAug 19, 2026

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions handling aliases and abbreviations and returning a list, which adds some behavioral context. However, it does not disclose what happens on no match, whether multiple matches are returned, or any edge-case behaviors. The openWorldHint annotation is the only structured signal, and the description does not contradict it but also does not enrich it significantly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that captures the essence, followed by a compact pricing metadata block. No wasted words; efficient and structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple single-parameter search tool with high schema coverage and an output schema, the description provides sufficient context. It covers the tool's function, input scope, and intended consumption. It lacks explicit disambiguation from the sibling search_company_candidates, but the name and description make the purpose clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema fully documents the 'text' parameter, including examples and purpose. The description adds no new parameter-level details beyond restating the function. Since schema coverage is 100%, the baseline of 3 applies; no compensation needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it 'resolves natural-language country or region names' against a 'geography registry' and returns 'standardized region names'. This is a specific verb-resource combination that distinguishes it from siblings like search_company_candidates and the report/visualization tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for resolving region names but does not explicitly mention when to use this tool over alternatives. It provides a usage context ('for downstream agent and MCP tool consumption') but lacks exclusions or direct comparisons to sibling tools. This is adequate but not explicit.

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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TDQS

B3/5.0

Disambiguation3/5

Tools are generally distinct in purpose (search, due diligence, tariff, supply chain), but the two 'search_company_candidates' and 'search_region_candidates' are similar in name and both serve lookup functions, and multiple tools like 'corporate_exception_report' and 'due_diligence_report' overlap in producing comprehensive company reports. The separation is clear enough for an agent but could cause confusion between similar-sounding search tools.

Naming Consistency3/5

Names mix styles: descriptive multi-word names (due_diligence_report, supply_chain_risk_prediction) and abbreviated names (sg_chokepoint, sg_visualization). There is no consistent verb_noun pattern, and the 'sg_' prefix is inconsistently applied. However, most names are readable and roughly follow a noun-based pattern.

Tool Count5/5

With 9 tools, the server covers a broad but coherent domain: search/identity, due diligence, supply chain analysis, and tariff compliance. Each tool addresses a distinct high-level capability, and the count feels appropriate for the scope—not too thin or overly heavy.

Completeness4/5

The tool surface covers major workflows: company identification, due diligence reporting, supply chain risk/chokepoint analysis, tariff classification and calculation. Minor gaps exist (e.g., no explicit update/delete on data since it's a read-only data provider), but for its purpose as an analysis API, it is reasonably complete.

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