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AssayChain — Mineral Intelligence (USGS and Field Runs)

stockpile_level

Read-onlyIdempotent

US Defense Logistics Agency (DLA) strategic stockpile check for 20 minerals. Returns whether held, quantity tonnes, and DLA stockpile status (active/disposed/not_held). Defense-contractor supply-chain agents use this to gauge DoD mineral security. Full data requires $0.25 USDC via GET /api/stockpile/{commodity} using x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commodityYesMineral commodity slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
heldYes
statusYes
paymentYes
commodityYes
paid_endpointYes

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds beyond that: the $0.25 USDC payment requirement, the GET endpoint using x402 on Base, and the returned data fields. This enriches the agent's understanding of cost and API usage.

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?

Three sentences, each with a distinct purpose: what the tool does, what it returns, and who uses it with cost details. No redundancy or fluff.

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 read-only check, the description covers purpose, outputs, payment, endpoint, and use case. With an output schema present and annotations clarifying safety, it leaves no critical gaps.

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 fully covers the commodity parameter with an enum and description. The description only mentions '20 minerals' without adding additional parameter-level semantics beyond the schema, so the baseline 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 identifies the tool as a DLA strategic stockpile check for 20 minerals and enumerates specific outputs (held status, quantity tonnes, DLA status). This distinguishes it from sibling supply-chain tools by its government stockpile focus and concrete return fields.

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?

It names the intended audience (defense-contractor supply-chain agents) and use case (gauge DoD mineral security), providing clear context for when to use. However, it does not explicitly exclude alternative tools or name when not to use it.

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.

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