Skip to main content
Glama

AssayChain — Mineral Intelligence (USGS and Field Runs)

benchmark.runs

Read-onlyIdempotent

Discovery preview for attested mineral-processing field run logs. Covers multiple rig modalities (mini-Duke elutriation, ultrasound-assisted grooved-tray gravity separation, and future rigs) under the same on-chain schema. Returns run count, run IDs, date range, and the paid REST endpoint. Full run data (feed parameters, tray geometry, ultrasound frequency where applicable, recovery rates, EAS attestation UIDs) requires $0.10 USDC via GET /api/benchmark/ultrasound-grooved-tray using the x402 protocol on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
attested_onlyNoIf true, count and list only runs that carry an on-chain EAS attestation UID.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentYes
run_idsYesRun ID list (YYYY-MM-DD-NNN format), newest first.
date_rangeYes
total_runsYesTotal run records matching the filter.
paid_endpointYesREST endpoint for the full paid response.
attested_countYesRuns that have an on-chain EAS attestation UID.

TDQS

A4.1/5.0
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 valuable behavioral context: it is a preview returning summary info, full data requires payment, and it covers multiple rig modalities including future rigs under the same schema. No contradictions 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 two sentences and front-loads the main purpose. The second sentence is dense but packed with relevant details (scope, return fields, paid endpoint). It does not waste words, though the density could be slightly improved by splitting.

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?

Given the tool's low complexity (1 optional param, output schema present), the description is quite complete. It covers purpose, return values, scope, and the path to full data. It does not describe output schema details, but that is covered by the output schema itself. Could mention that the preview is free, but that is implied by the paid full data endpoint.

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 the only parameter (`attested_only`) clearly described in the schema. The description does not add much parameter-specific meaning, though it mentions 'attested' logs and EAS attestation UIDs in the full data context, indirectly relating to the parameter. Baseline 3 is appropriate given full schema coverage.

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 the tool's function as a 'Discovery preview' for 'attested mineral-processing field run logs', specifying what it returns (run count, run IDs, date range, paid REST endpoint). It distinguishes itself from the full data endpoint and sibling tools by emphasizing its preview nature and coverage of multiple rig modalities.

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 clearly indicates when this tool should be used: for a quick overview/preview, while full run data requires a paid REST endpoint ($0.10 USDC via x402). It does not explicitly name sibling tools as alternatives, but the contrast with the paid endpoint provides clear usage context.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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