AssayChain — Mineral Intelligence (USGS and Field Runs)

corpus.search

Read-onlyIdempotent

Free lexical search (BM25-lite) across all 199 EAS-attested files: 20 USGS critical-mineral commodity benchmarks + 179 US/MX mining district records. Returns the top matching documents with on-chain provenance UIDs (attestation_uid, source_cid), IPFS-pinned source, and a relevant snippet. Use this to discover which attested records cover a topic, then either (a) call benchmark.commodity / district.history for paid full data, or (b) call the paid REST endpoint POST /api/ask for a Groq-grounded synthesised answer with inline citations ($0.10 USDC via x402 on Base).

Input Schema

TableJSON Schema

Name	Required	Description
`kind`	No	Optional filter to restrict the corpus to commodities or districts only.
`query`	Yes	Free-text query — e.g. 'arsenic penalty copper smelter', 'gallium export controls China', 'silver veins Philipsburg Montana'.
`top_k`	No	Number of hits to return (1–20).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`hits`	Yes
`query`	Yes
`corpus`	Yes
`payment`	Yes
`paid_endpoints`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful context about return content (provenance UIDs, IPFS-pinned source, snippet) and characterizes the search as 'BM25-lite', hinting at approximate ranking. It doesn't contradict annotations.

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?

Two dense sentences: first states what the tool does and returns, second gives usage guidance. Every clause earns its place with specific details and alternatives. No 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?

Given the tool has rich output schema, annotations, and full schema parameter coverage, the description still adds critical context: the corpus size, what provenance UIDs guarantee, and how to proceed after search. It is complete for an agent to select and invoke 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?

Schema description coverage is 100%, with all three parameters (query, kind, top_k) well-documented in the schema. The description does not add parameter-level detail beyond what the schema provides, but it reinforces the intended use of the query parameter with contextual follow-up actions. Baseline 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 a specific verb ('Free lexical search') and resource ('all 199 EAS-attested files'), with a defined scope (20 USGS benchmarks + 179 district records). It also distinguishes from siblings by naming alternatives (benchmark.commodity, district.history) and the paid REST endpoint.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Use this to discover which attested records cover a topic', followed by two concrete follow-up options (paid full data or paid synth answer). This tells the agent when to use this tool versus alternatives.

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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