Get energy prices from the U.S. Energy Information Administration. WTI crude, Brent crude, natural gas.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Weekly data points | |
| series | No | wti_crude (default), brent_crude, natural_gas | wti_crude |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states that the tool gets prices, but fails to disclose key behavioral traits such as data frequency, update lag, authentication requirements, or whether the response is a single value or time series. This lack of detail hinders an agent from understanding side effects or constraints.
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 very concise, consisting of two short sentences that front-load the main purpose and list the supported resources. It wastes no words, though it could be slightly more structured with a separator between the purpose and the list.
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 no output schema and no annotations, the description should provide more context about the return format, units, or pricing type. It covers the basics but leaves uncertainty about what data exactly will be returned, making it marginally complete for an agent.
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 coverage is 100%, so the baseline is 3. The description adds minimal value over the schema—it repeats the series options ('wti_crude, brent_crude, natural_gas') and mentions 'Weekly data points' for limit, which is already in the schema description. No further context is provided.
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 clearly states the tool retrieves energy prices from a specific source (U.S. Energy Information Administration) and lists the specific commodities (WTI crude, Brent crude, natural gas). This distinguishes it from sibling tools like get_inflation or get_jobs, which cover different domains.
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?
The description implicitly advises using this tool when energy price data is needed. It does not explicitly state when not to use it or provide alternatives, but the clear purpose makes usage straightforward.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct economic domain (e.g., demographics, inflation, jobs) with no overlap. The descriptions clearly differentiate the data sources and use cases.
All tools follow a consistent `get_<topic>` pattern using snake_case, making the naming predictable and easy to navigate.
With 7 tools, the server provides a focused yet comprehensive set of macroeconomic indicators without being overwhelming. Each tool serves a clear purpose.
Covers major macroeconomic areas (employment, inflation, GDP, demographics, energy, treasury) but lacks minor categories like trade or consumer confidence. Gaps are slight.