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SupplyGraph.AI

by io.github.SupplyGraphAI

Search Company Candidates

search_company_candidates

Search company candidates by company name, optionally filtered by country or region, and return possible matching records with mapped company IDs for caller-side selection.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesCompany name with optional country or region. The input should contain a company name, and may optionally include its country or region (e.g. 'Tesla United States', 'Samsung South Korea', 'Huawei China').

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

The description mentions that results are for caller-side selection, implying it returns multiple candidates rather than a single definitive match. It also includes pricing information (1 credit per run), which is useful. However, it does not disclose details like rate limits, error behavior, or what happens if no matches are found. The openWorldHint annotation is present but the description adds some context beyond it.

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 concise, with a single clear sentence followed by pricing information. It is front-loaded with the core purpose and includes examples in the schema. No wasted words.

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?

The tool has a simple interface (one parameter) and an output schema, so the description doesn't need to explain return values. The description covers the purpose, input format, and pricing. It could mention what happens with ambiguous matches or no results, but given the simplicity and output schema, it is largely complete.

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 already provides 100% coverage of the single parameter 'text' with a detailed description and examples. The tool description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.

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 searches company candidates by company name with optional country/region filters, and returns matching records with mapped company IDs for caller-side selection. This distinguishes it from siblings like search_region_candidates, which likely searches by region instead.

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

Usage Guidelines4/5

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

The description explains the input format (company name with optional country/region) and provides examples, which implies when to use it. However, it does not explicitly state when not to use it or mention alternatives like search_region_candidates, though the sibling list makes the distinction inferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All SupplyGraph.AI toolsInspect
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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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