SupplyGraph.AI

Supply Chain Risk Prediction Agent

supply_chain_risk_prediction

Continuously monitors global supply chain risk events and evaluates whether, how, and to what extent those events may affect a target company.

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "event_info": "<event_info>"
    },
    "name": "supply_chain_risk_prediction"
  }
}

Input Schema

Name	Required	Description	Default
`event_info`	Yes	Evaluates how a supply chain risk event may affect a target company through multi-tier supply chain propagation analysis.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 19, 2026

TDQS

A3.6/5.0

Behavior2/5

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

The description lacks transparency about operational aspects such as side effects, return value, or whether 'continuously monitors' implies an ongoing process or a single analysis. The only annotation (openWorldHint) does not compensate for this lack of behavioral detail.

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 succinct and to the point, consisting of a single clear sentence. It avoids redundancy with the schema details and is well-structured.

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

Completeness3/5

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

While the schema is detailed, the description lacks information about the expected output or how to interpret results. It also does not explain the practical context for the different event types or analysis modes, which are crucial for a complete understanding of the tool's usage.

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

Parameters5/5

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

The schema itself provides comprehensive descriptions for all nested properties, and the description of the top-level 'event_info' adds meaningful context about multi-tier supply chain propagation analysis. This fully clarifies the purpose of each parameter.

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: continuously monitors global supply chain risk events and evaluates their impact on a target company. This is specific and distinct from sibling tools like due_diligence_report or tariff_calc.

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?

The description does not provide explicit guidance on when to use this tool versus alternatives. It mentions 'continuously monitors' but does not specify under what conditions this tool should be invoked or when other tools might be more appropriate.

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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