Skip to main content
Glama

Commodity Indicators

commodities
Read-only

Get historical price series for supported commodity indicators using the exact slugs advertised by this schema. Requires an API key. Supported indicators: crude_oil_inventories, gold, natural_gas, natural_gas_storage, oil_brent, oil_wti, platinum, silver.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNoBackward-compatible alias for `indicator`. Prefer `indicator` in new calls.
end_dateNoInclusive upper bound, YYYY-MM-DD.
indicatorNoCommodity indicator slug. Supported: crude_oil_inventories, gold, natural_gas, natural_gas_storage, oil_brent, oil_wti, platinum, silver.
start_dateNoInclusive lower bound, YYYY-MM-DD.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish a safe read-only operation, and the description adds value by disclosing the API-key requirement—a behavioral prerequisite not visible in annotations. It also reinforces deterministic lookup via exact slugs, but does not detail response shape or error behavior; the output schema mitigates this.

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?

The description is front-loaded with the core action, followed by the API-key note and a compact list of supported indicators. Every sentence earns its place, though the indicator list is somewhat redundant with the schema.

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?

For a four-parameter, read-only tool with a full output schema and annotations, the description covers the essential selection and auth context. The omission of return-value details is acceptable because the output schema exists; it is complete enough 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?

All four parameters have descriptions in the schema, including date formats and the symbol/indicator alias precedence, so the schema covers 100% of parameter semantics. The description's list of supported indicators duplicates the schema's examples but usefully emphasizes using exact advertised slugs, adding no new syntax details.

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 opens with a specific verb ('Get') and resource ('historical price series for supported commodity indicators'), distinguishing it from siblings like 'latest_commodities' by emphasizing historical data. It also enumerates the exact supported slugs, leaving no ambiguity about scope.

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?

It states the tool is for historical price series and notes an API key requirement, giving clear context for when to call it. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full exclusionary guidance.

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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions; the visual_artifact variants are explicitly duplicate payloads for chart rendering. However, several task and analysis tools (macro_briefing_task, macro_research_pack_task, indicator_intel_task) have overlapping scopes and could cause misselection despite different outputs.

Naming Consistency4/5

Tool names are consistently snake_case with systematic _task and _visual_artifact suffixes, making the pattern predictable. Minor deviations like 'ping', 'subscribe_for_mcp_access', and a few noun-only names (e.g., 'forex', 'commodities') break a strict verb_noun pattern but remain readable.

Tool Count2/5

At 48 tools, the surface is far beyond the typical well-scoped server and risks overwhelming agents. The broad macro/FX domain justifies some size, but 48 is excessive and could be consolidated (e.g., merging visual artifact pairs or grouping task tools).

Completeness5/5

The tool set covers the full macro/FX workflow: data discovery (data_catalogue), raw queries (indicator_query, forex, commodities), visual artifacts, release calendar, news, COT, sentiment, seasonality, backtesting, scenario modeling, portfolio risk, and reference tools. No obvious dead ends or missing lifecycle operations for a read-heavy data server.