fed_rates
Federal Reserve interest rates, FOMC meeting calendar, policy outlook.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Federal Reserve interest rates, FOMC meeting calendar, policy outlook.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / additionalPropertiesAdded value: +falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only lists data topics. It does not state whether the tool is read-only, what data source or freshness applies, how results are returned, or any limitations. The phrase 'policy outlook' is ambiguous and lacks behavioral detail.
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 concise and front-loaded, containing zero filler words. However, it is a noun phrase rather than a complete sentence, and the lack of a verb reduces structural clarity. It is appropriately short but could be more informative.
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 simplicity (no parameters, no output schema), the description provides a minimal list of contents but is vague about 'policy outlook' and does not clarify version differences from fed_rates_v2/v3. It is adequate for a basic data feed but leaves room for more specificity about return values and coverage.
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 tool has zero parameters, and the schema is empty. Per the rubric, the baseline is 4 for 0-parameter tools. The description adds contextual meaning by specifying the data domain (rates, calendar, outlook), which is useful for a parameterless tool.
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 identifies the resource (Federal Reserve interest rates, FOMC meeting calendar, policy outlook) but lacks a specific verb or action. It is clear in topic but does not state what the tool actually does (e.g., returns, provides, monitors), and it does not distinguish from sibling tools like fed_rates_v2 or fed_rates_v3.
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?
There is no explicit guidance on when to use this tool versus alternatives. The description implies it is relevant to Fed rates and policy information, but it does not mention exclusions, prerequisites, or how it compares to the many related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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