Fetch aluminum commodity data for financial analysis, including real-time and historical prices with customizable intervals and data formats.
Fetch aluminum
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": "aluminum"
}
}| 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 full burden for behavioral disclosure. 'Fetch aluminum' implies a read-only operation but doesn't specify what is fetched, how data is returned, any rate limits, authentication needs, or error conditions. For a tool with 2 parameters and no output schema, this is inadequate, offering no behavioral insights beyond the basic verb.
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 two words, 'Fetch aluminum', which is front-loaded and wastes no space. While under-specified, it earns a high score for brevity and lack of redundancy, as every word directly relates to the tool's function without unnecessary elaboration.
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 (2 parameters, 0% schema coverage, no annotations, no output schema, and many sibling tools), the description is completely inadequate. It doesn't explain what 'aluminum' refers to, how to use the parameters, what the output looks like, or when to choose this over other tools. This leaves critical gaps for an AI agent to understand and invoke the tool correctly.
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?
Schema description coverage is 0%, meaning parameters 'interval' and 'datatype' are undocumented in the schema. The description 'Fetch aluminum' adds no information about these parameters—it doesn't explain their purpose, expected values, or how they affect the fetch operation. With low coverage and no compensation in the description, this fails to provide any parameter semantics.
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 aluminum' restates the tool name 'aluminum' with a generic verb 'fetch', making it tautological. It lacks specificity about what resource is being fetched (e.g., price data, market info, production stats) and doesn't distinguish it from sibling tools like 'copper' or 'wheat', which likely have similar vague descriptions. This provides minimal clarity beyond the name.
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. The description doesn't mention context, prerequisites, or comparisons to sibling tools (e.g., other commodity tools like 'copper' or 'corn'), leaving the agent with no usage instructions. This is a complete lack of guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.