BLM LR2000 mining claims data by state and county. Returns active lode/placer/millsite counts for US mining counties. $0.25 per query.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | US state code (e.g., NV, AZ, CA) | |
| county | Yes | County name slug (e.g., Elko, Pima) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the $0.25 per query cost and specifies that it returns active counts, but it does not mention error handling, rate limits, or limitations beyond 'US mining counties'. This is moderate disclosure but leaves some behavioral aspects uncovered.
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, each adding distinct value: data source and scope, return type, and cost. It is front-loaded and free of unnecessary words, making it highly concise and well-structured.
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?
Given no output schema, the description adequately explains the return type (active lode/placer/millsite counts) and includes cost information. It could mention output format or error conditions, but for a simple two-parameter query tool, it covers the essential context.
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 provides complete descriptions for both parameters, including examples. The tool description adds no additional parameter semantics, but since schema coverage is 100%, 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 BLM LR2000 mining claims data by state and county, specifically active lode/placer/millsite counts. This includes a specific verb ('returns'), the resource ('BLM LR2000 mining claims'), and a clear scope, distinguishing it from sibling tools.
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 implies usage for retrieving BLM mining claims data, but it does not explicitly state when to use it versus alternatives or provide exclusion criteria. The cost and data type offer context, but there is no direct 'when to use' 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.
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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.