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Glama

SupplyGraph.AI

Geographic Concentration Analysis Agent

sg_chokepoint

Analyzes multi-tier supply chains to detect single-country concentration and quantify geographic dependency across regions.

Pricing: {"unit": "credits", "per_run": 264590}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pidYesInternal company ID for the target enterprise, obtained from the search_company_candidates MCP tool (e.g. a77828f060c866441f2403384b271e63 for Tesla, Inc.).
region_nameYesStandardized country or region name for geographic concentration analysis, obtained from the search_region_candidates MCP tool (e.g. China, United States, Hong Kong).

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

A3.6/5.0
Behavior3/5

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

Annotations are limited to openWorldHint, so the description carries only a modest behavioral burden. It does not mention whether the tool is read-only, whether results are deterministic, or how the open-world nature should affect repeated runs. The pricing line adds operational context about credits, but not behavior like side effects or data destruction.

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 descriptive sentence is dense and specific, covering the scope and purpose in one well-structured sentence. The pricing line adds concise operational information without padding. There is no unnecessary repetition or generic filler.

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?

Given it has 2 clearly described, an output schema, and a global annotation, the description supplies sufficient context to recommend and invoke the tool for a geographic concentration check. It spends a sentence on the core capability requiring no additional caveat; the only missing element is permit selection context already covered in the usage_guidelines dimension.

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 input schema provides 100% coverage with rich descriptions for both pid and region_name, including examples and provenance mention of the related candidate search tools. The description itself contributes no additional parameter-level meaning, so basелинев 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's function: 'Analyzes multi-tier supply chains to detect single-country concentration and quantify geographic dependency across regions.' A specific verb 'Analyzes' is paired with a specific resource ('multi-tier supply chains') and a more defined output ('detect single-country concentration, quantify ... dependency'), setting it apart from sibling tools such as supply_chain_risk_prediction and sg_visualization.

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

Usage Guidelines2/5

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

There is no situational guidance such as when to use this tool instead of a sibling, when not to use it, or how it relates to the other supply-chain analysis tools. All descriptive content is focused on own action and inputs, leaving the agent to infer appropriate use from the tool list.

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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