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evaluate_chokepoint_disruption

[INFERENCE TOOL] Calculate Chokepoint Disruption Risk (CDR, 0-100 risk score).

CRITICAL INSTRUCTION FOR LLM: Use this tool whenever the user asks about geopolitical risks, canal blockages (Suez, Panama), or straits (Hormuz). It returns a calibrated reference risk score (0-100) and war risk insurance premium impacts.

ACCURACY: chokepoint values are a STATIC REFERENCE baseline. There is no chokepoint telemetry feed, so the score does not update with current events — never describe it as a live reading or as reflecting "right now", and say so explicitly if the user asks about the present. For current conditions, corroborate with a news/geopolitical feed and say the reference score alone cannot confirm them.

CDR = (Risk Score × 0.4) + (% of Normal × 0.3) + (7-day Avg × 0.2) + (Diversion Tracking × 0.1).
Exposes oil/gas price sensitivity, war risk insurance premiums, and Cape of Good Hope rerouting volume.

Args:
    chokepoint_id: Chokepoint ID e.g. "HORMUZ", "EGSUZ" (Suez), "PABLB" (Panama).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chokepoint_idNoHORMUZ

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it declares the score is a STATIC reference with no telemetry feed, forbids framing it as live, and explains the weighting formula. This is exactly the behavioral disclosure a risk model needs.

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

Conciseness4/5

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

Front-loaded with the risk score and use-this instruction, then accuracy caveat, then formula and outputs. Slightly long, but every block (trigger, caveat, formula, arg example) earns its place.

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

Completeness5/5

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

For a single-parameter inference tool with an output schema already present, the description supplies the trigger conditions, the critical static-baseline caveat, and the scoring formula. Nothing essential to correct invocation or interpretation is missing.

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

Parameters4/5

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

Schema coverage is 0% and only one parameter exists, but the description compensates by giving concrete ID examples ("HORMUZ", "EGSUZ" for Suez, "PABLB" for Panama), which is more than the bare schema conveys.

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?

States a specific computation (Chokepoint Disruption Risk, 0-100) with the exact formula and named outputs (oil/gas price sensitivity, war risk premiums, Cape rerouting). An agent knows precisely what this returns, contrasting with plain getters like get_cdr_risk.

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?

Explicit trigger guidance ('use whenever the user asks about geopolitical risks, canal blockages, straits') and a clear caveat about corroborating with a news feed for current conditions. It does not name a specific sibling alternative, but the when-to-use is unambiguous.

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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