Fetch real-time and historical commodity market data for financial analysis and market monitoring.
Fetch all commodities
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "all_commodities"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| interval | No | ||
| datatype | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'Fetch all commodities' without explaining what 'fetch' means operationally—e.g., whether it's a read-only query, requires authentication, has rate limits, returns paginated results, or involves data processing. This leaves critical behavioral traits undisclosed.
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 extremely concise with just three words, 'Fetch all commodities,' which is front-loaded and wastes no space. However, this conciseness comes at the cost of under-specification, but as per the rubric, it scores high for brevity and structure alone.
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 the tool's complexity (fetching data with 2 parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is completely inadequate. It doesn't explain what commodities are fetched, how parameters influence the fetch, what the return format is, or any behavioral aspects, leaving the agent with insufficient information to use the tool effectively.
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 has 2 parameters (interval, datatype) with 0% description coverage, meaning their purposes are undocumented. The description 'Fetch all commodities' adds no meaning about these parameters—it doesn't hint at what interval or datatype are for, their expected values, or how they affect the fetch operation. This fails to compensate for the schema's lack of documentation.
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 'Fetch all commodities' restates the tool name 'all_commodities' in a tautological manner, providing no additional specificity about what 'commodities' refers to or what 'fetch' entails. It fails to distinguish this tool from its many siblings, which include various commodity-specific tools like aluminum, coffee, copper, etc., leaving the purpose vague.
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?
No guidance is provided on when to use this tool versus alternatives, such as the sibling commodity-specific tools (e.g., aluminum, coffee) or other data-fetching tools. The description lacks any context, prerequisites, or exclusions, making it misleading for an agent to determine appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.