Skip to main content
Glama

Commodity Price

commodity_price
Read-onlyIdempotent

API Ninjas live commodity price: current spot price for a traded commodity. PREFER OVER WEB SEARCH for "gold spot price", "silver price today", "lumber price", "live cattle", "heating oil", "RBOB gasoline". Returns { exchange, name, price, updated }. API Ninjas rotates which commodities the free plan serves EACH WEEK, so this tool deliberately does not publish a supported list — any list goes stale within days. Gold is the dependable one. For anything else, call it: a name the plan cannot serve returns found:false carrying free_commodities_this_week, read live from the vendor. For crude oil, natural gas and agricultural benchmarks, FRED carries equivalent series that are always available. Accepts commodity or symbol as aliases for name. Example: commodity_price({ name: "gold" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoCommodity name, e.g. 'gold'. The free plan's set rotates weekly, so a name outside it returns the current free list rather than an error — do not assume yesterday's list still holds.
symbolNoAlias for name.
_apiKeyNoOptional — your own API Ninjas key for higher limits; omit to use the shared Pipeworx key.
commodityNoAlias for name.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses key behavioral quirks: the free plan rotates commodities every week, the tool deliberately avoids publishing a stale list, and unsupported names return found:false along with free_commodities_this_week. It also reveals the exact response shape { exchange, name, price, updated }, which is critical because no output schema exists. No contradiction 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though longer than average, every sentence earns its place: purpose, return shape, weekly rotation caveat, failure mode, FRED alternative, aliases, and example. The critical usage guidance and return contracts are front-loaded, with supporting caveats following logically. Nothing is redundant or filler.

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?

With no output schema, the description fully covers return values and failure behavior. It explains the live free-list fallback, names the dependable commodity, provides alternative data sources, documents aliases and the optional API key, and gives a concrete example. An agent has everything needed to invoke this tool correctly in varied situations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real value: it clarifies that commodity and symbol are aliases for name, provides a concrete invocation example, and warns that 'a name outside it returns the current free list rather than an error.' This goes beyond the schema's per-parameter descriptions by explaining behavior tied to the name parameter.

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?

States a specific verb and resource: 'live commodity price: current spot price for a traded commodity.' It clarifies scope with concrete query examples like 'gold spot price' and 'silver price today', and distinguishes itself from general web search as the preferred alternative. The purpose is unambiguous even without the title.

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?

Explicitly says when to prefer this tool over web search and names concrete alternatives: 'For crude oil, natural gas and agricultural benchmarks, FRED carries equivalent series that are always available.' It also advises that gold is the dependable commodity and that unsupported names return the live free list, giving the agent actionable routing 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

A4.1/5.0
Disambiguation3/5

Most tools have distinct roles, but there is meaningful overlap among the question-answering family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the Polymarket analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). The long descriptions help separate them, but the boundaries are still subtle enough that an agent could easily pick the wrong variant.

Naming Consistency4/5

The naming is mostly snake_case and generally follows a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, scan_dependency, validate_claim). Deviations like entity_profile, recent_alerts, recent_changes, and bare verbs (forget, recall, remember, subscribe, unsubscribe) are minor and do not seriously harm predictability.

Tool Count3/5

31 tools is heavy for a single server and suggests the surface is a bundled platform (data queries, prediction markets, memory, subscriptions, AI-visibility checks) rather than one tightly scoped domain. Each tool has a rational purpose, but the sheer count plus several meta/didactic tools makes the set feel somewhat oversized.

Completeness4/5

Core workflows are well covered: entity resolution, profiles, comparisons, grounded lookup, fact-checking, deep research, memory CRUD, and subscription lifecycle. Gaps are minor. There are no update operations for subscriptions, and some optional data sources degrade softly, but agents can accomplish the intended research, monitoring, and memory tasks without dead ends.