Fetch real-time and historical natural gas market data from Alpha Vantage for financial analysis and market monitoring.
Fetch natural gas
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": "natural_gas"
}
}| 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. 'Fetch' implies a read operation, but it fails to describe any behavioral traits such as data sources, rate limits, authentication needs, or output format. This leaves critical gaps for safe and effective tool invocation.
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 two words, 'Fetch natural gas', which is front-loaded and wastes no space. However, this brevity comes at the cost of under-specification, but it meets the criteria for conciseness by avoiding 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 complexity of a commodity data tool with 2 parameters, no annotations, no output schema, and low schema coverage, the description is completely inadequate. It does not provide enough information for an agent to understand what the tool does, how to use it, or what to expect in return.
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 adds no meaning beyond the schema, not explaining what these parameters control or their expected values, failing to compensate for the low coverage.
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 natural gas' restates the tool name 'natural_gas' with a generic verb 'fetch', making it tautological. It lacks specificity about what aspect of natural gas is fetched (e.g., prices, volumes, forecasts) and does not distinguish it from sibling tools like 'brent_crude_oil' or 'wti_crude_oil', which suggests it's part of a commodity data family without clear differentiation.
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. With many sibling tools for commodities and analytics, the description offers no context, prerequisites, or exclusions, leaving the agent to guess based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.