Average interest rates the US Treasury pays on its marketable and non-marketable securities (Treasury Bills, Notes, Bonds, TIPS, etc.), by month. Optionally filter by security description.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. | |
| security | No | Filter by security type/description, e.g. 'Treasury Notes', 'Bills', 'TIPS'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is well covered. The description adds context about the data being monthly and the optional filter, but it does not disclose behaviors like pagination, default limits, or time span, which are not critical given the simple read-only nature.
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 a single, clear sentence with no redundant words. It front-loads the core purpose (average interest rates) and specifies the data granularity and optional filter efficiently. No unnecessary detail or filler.
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?
For a simple, read-only tool with two optional parameters and no output schema, the description adequately explains what data is returned and the optional filter. It does not mention limit behavior or historical coverage, but these are minor gaps given the simplicity and existing annotation coverage. The essential information for calling the tool is present.
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 schema provides 100% coverage for both parameters (limit and security). The description adds a brief note about filtering by security description, aligning with the 'security' parameter, but does not elaborate on format or behavior beyond what the schema already states. No additional semantic value 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 returns average interest rates for US Treasury securities (marketable and non-marketable), including specific types like Treasury Bills, Notes, Bonds, and TIPS, on a monthly basis. This is specific and distinguishes it from sibling treasury tools such as treasury_auctions and treasury_cash_balance by focusing on interest rates.
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 implies usage for interest rate data but does not explicitly mention when to use this tool versus alternatives. No exclusions or comparisons to other treasury tools are provided, leaving the selection rationale to the agent's inference from the tool name and 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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.