get_treasury_yield_by_dateReturns the 30-year US Treasury yield for a specific date. Business days only. Use YYYY-MM-DD format.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date in YYYY-MM-DD format e.g. 2026-05-09 |
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date of the observation (YYYY-MM-DD) | |
| yield_percent | Yes | 30-year Treasury yield as a percentage |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description need not repeat safety. It adds the business-days-only constraint, which is useful. Does not explain behavior for non-business days, but that is a minor gap given annotations cover safety.
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?
Two sentences, no wasted words. States purpose, constraint, and format efficiently. Front-loaded with the core action and object.
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?
Simple tool with one param, full schema coverage, and output schema present. Description covers the essential constraint (business days) and format. Output schema handles return value details, so no additional need.
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% and description reiterates the date format already present in the schema pattern. No additional semantic meaning beyond what the schema provides, so baseline 3 is appropriate.
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?
Description uses specific verb 'Returns' with explicit resource '30-year US Treasury yield' and scope 'for a specific date'. Clearly distinguishes from siblings like get_treasury_yield_current (current) and get_yield_curve (curve).
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?
States 'Business days only' and date format, giving clear usage constraints. Does not explicitly name alternatives or when not to use, but the tool name and context make the specific-date use case obvious. Lacks explicit exclusions but provides adequate context.
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.
The tool set has several overlapping functions, particularly among the bundle tools (e.g., get_bundle_context_brief vs get_bundle_macro both provide macro indicators, and get_bundle_rate_environment overlaps with get_yield_curve and get_policy_spread). Individual current/history/date tools are distinct but some redundancy exists (e.g., get_treasury_yield_current vs get_current_value for DGS30). Descriptions help differentiate purposes, but agents may still hesitate when selecting between similar bundles.
All tool names begin with the verb 'get_' and use snake_case, creating a consistent pattern. The bundle tools are uniformly prefixed with 'get_bundle_', and individual data tools follow a get_[entity]_[modifier] structure (e.g., get_fx_rate_current, get_fx_rate_by_date, get_fx_rate_series). Minor deviations exist (e.g., get_series, get_current_value, get_value_by_date are less descriptive of the underlying entity), but overall the naming is predictable and readable.
With 24 tools, the server is on the heavy side of the typical range. The broad domain (macro data, mortgage, crypto, FX, treasury, EDGAR) justifies many tools, but some could potentially be consolidated (e.g., individual rate tools vs rate bundles). The count does not feel overwhelming, but it is borderline heavy.
The server covers a wide range of economic and financial data with both bundled and granular views. It includes current, historical, and date-specific retrievals for FRED series, FX, and crypto, plus specialized tools for mortgages, recession, and yield curves. Minor gaps exist, such as no way to list all supported FRED series or full financial statements for EDGAR, but the core lifecycle of data querying is well covered.