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AssayChain — Mineral Intelligence (USGS and Field Runs)

supply_resilience

Read-onlyIdempotent

Supply chain resilience score + scenario analysis for 20 critical minerals (graphite, cobalt, lithium, gallium, etc.). Returns a 0-100 score, vulnerability level, 5-factor breakdown (supply concentration, allied production, export restrictions, stockpile coverage, geopolitical criticality), and 3 scenario tests (China export ban, DLA depletion, allied disruption). Procurement agents and supply-chain strategists use this for vulnerability triage. Full data requires $0.50 USDC via GET /api/resilience/{commodity} using x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commodityYesMineral commodity slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentYes
commodityYes
paid_endpointYes
resilience_levelYes
resilience_scoreYes

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and non-destructive, but the description adds important behavioral context: the $0.50 USDC payment requirement and the use of x402 on Base. It also describes the return structure. No contradictions with annotations. It doesn't cover error handling or rate limits, but the added payment and endpoint details merit a 4.

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?

The description is three sentences long, each earning its place: purpose and outputs, target users, and payment/endpoint. It is front-loaded with the core functionality. Slightly long for a simple tool, but the complexity of the outputs justifies the length.

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?

With an output schema present, the description sufficiently covers the tool's purpose, outputs, use case, and payment requirement. It lacks potential edge-case details (e.g., partial data behavior) but overall provides a complete picture for an agent to invoke it correctly.

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 provides 100% coverage with a full enum for 'commodity' and a clear description ('Mineral commodity slug.'). The tool description adds only general context (e.g., '20 critical minerals') but no additional parameter semantics. Baseline 3 is appropriate because the schema already carries the weight.

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 states a specific verb and resource: 'Supply chain resilience score + scenario analysis for 20 critical minerals.' It enumerates concrete outputs (0-100 score, vulnerability level, 5-factor breakdown, 3 scenario tests) that clearly distinguish it from siblings like supply_share and stockpile_level. The intended audience (procurement agents, supply-chain strategists) further clarifies its purpose.

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 provides clear context: 'Procurement agents and supply-chain strategists use this for vulnerability triage.' This implies the tool is for resilience assessment rather than raw data lookup. However, it does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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

A3.6/5.0
Disambiguation4/5

Most tools target distinct data domains (operations, claims, surveys, supply, ESG, extraction). The three benchmark.* tools are clearly grouped, and supply_resilience vs supply_share are distinct in focus. Minor overlap exists between earth_mri_focus_areas and geophysics_surveys, both covering geophysical survey data.

Naming Consistency2/5

Tool names mix snake_case (active_operations, blm_claims_data) with dotted namespaces (benchmark.commodity, extract.estimate, sales.ask). There is no consistent verb_noun pattern; many are noun phrases, some are verb-led. This inconsistency makes the API surface harder to learn.

Tool Count3/5

At 19 tools, the server spans a wide domain (commodity benchmarks, extraction workflow, ESG, supply chain, surveys). While each tool has a clear role, the count exceeds the typical well-scoped range, making the surface feel heavy for an MCP server.

Completeness4/5

The tool set covers most critical mineral intelligence functions: operations, claims, surveys, benchmarks, criticality, trade, stockpile, supply, ESG, search, and extraction. Missing a direct full-data retrieval tool (payments happen via external REST endpoints) and a consolidated commodity report tool, but the coverage is strong.

Resources