AssayChain — Mineral Intelligence (USGS and Field Runs)

district.history

Read-onlyIdempotent

Free preview of a US or Mexico mining district record (MRDS-sourced). Returns field inventory, commodity summary, discovery year, and deposit count. Useful for domestic-sourcing due diligence (DoD/DFC project assessments, UFLPA country-of-origin research), historic production context, and mining project developer research. Full record (deposits[], geology, sources[], history narrative) requires $0.50 USDC via GET /api/historical/{country}/{state}/{county}/{district} using x402 on Base.

Input Schema

TableJSON Schema

Name	Required	Description
`state`	Yes	State/province code, e.g. 'MT', 'NV', 'SON'
`county`	Yes	County or equivalent name, e.g. 'Missoula'
`country`	Yes	ISO 3166-1 alpha-2 country code, e.g. 'US', 'CA', 'MX'
`district`	Yes	District name or slug, e.g. 'Coloma' or 'Helena'

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`state`	Yes
`county`	Yes
`country`	Yes
`payment`	Yes
`district`	Yes
`has_history`	Yes
`deposit_count`	Yes
`paid_endpoint`	Yes
`discovery_year`	Yes
`attestation_uid`	Yes
`available_fields`	Yes
`development_status`	Yes
`district_alt_names`	Yes
`primary_commodities`	Yes
`secondary_commodities`	Yes

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish read-only and idempotent behavior. The description adds value by revealing the paywall for full data, the MRDS source, and the specific fields returned in the free preview. No contradiction with annotations.

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 concise and front-loaded with the core purpose, followed by use cases and payment conditions. It is slightly long but every sentence serves a clear function.

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?

The description covers purpose, return contents, use cases, and the premium full-record option. An output schema exists, so return-value details need not be spelled out. Overall, it is complete for a simple read-only preview tool.

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 fully describes all four parameters with examples, and the description does not add further parameter-level detail. With 100% schema coverage, 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 states that this tool provides a free preview of mining district records for the US or Mexico, with specific contents (field inventory, commodity summary, discovery year, deposit count). This directly differentiates it from the paid full-record endpoint and any sibling tools.

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 identifies clear use cases (DoD/DFC project assessments, UFLPA research, production context, developer research) and contrasts the free preview with the paid full record. It does not explicitly name alternative tools, but the context is strong enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AssayChain — Mineral Intelligence (USGS and Field Runs) tools Inspect

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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

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