Historical geophysical survey metadata for US mining districts. Returns aeromag, gravity, EM, and radiometric survey details. $0.25 per query.
| Name | Required | Description | Default |
|---|---|---|---|
| district | Yes | District slug (e.g., philipsburg, ajo, bingham) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavioral traits. It says 'Returns' which implies a read-only operation, and it discloses the cost. However, it does not mention error handling, rate limits, or explicit safety characteristics, so transparency is adequate but limited.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences: it states the subject, the contents, and the cost. Every word earns its place, with no fluff or redundancy. The purpose is front-loaded, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with one parameter and no output schema, the description covers the essential context: what it returns, the geographic scope, and the cost. It does not describe the output format or edge cases, but these are less critical given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the sole 'district' parameter with examples and a clear description (100% coverage). The tool description adds no additional parameter semantics beyond what the schema already provides, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it returns historical geophysical survey metadata for US mining districts, specifying the types (aeromag, gravity, EM, radiometric). This specific verb+resource+scope distinguishes it from sibling tools like active_operations or supply_resilience, which are not focused on geophysical data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on what data it covers (US mining districts, specific survey types) and mentions the cost per query, which helps decide when to use it. However, it does not explicitly state when not to use it or mention alternative tools, so it lacks exclusions and alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.