Recent US Treasury securities auction results: term, CUSIP, issue/maturity dates, high yield, interest rate, bid-to-cover ratio, and amounts. Optionally filter by security type (Bill, Note, Bond, TIPS, FRN).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. | |
| security_type | No | Filter by security type: 'Bill', 'Note', 'Bond', 'TIPS', 'FRN'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already communicate read-only, idempotent, non-destructive behavior. The description adds useful context on the output fields and filter option, but leaves 'recent' undefined and does not disclose ordering, default limit, or pagination behavior.
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 compact sentences front-load the core resource and field list, then state the only optional filter. Every sentence contributes information and no extraneous text is present.
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 zero required parameters and full schema coverage, the description adequately lists return fields and the filter option. The main gap is the vague meaning of 'recent' and lack of a default-limit or ordering note, but these are not critical for basic usage.
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 description coverage is 100%: limit is documented as 'Max rows to return' and security_type lists the valid values. The description only restates the security_type values, adding no additional parameter semantics beyond the schema.
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 names the resource (recent US Treasury auction results) and enumerates the returned fields (term, CUSIP, dates, yield, bid-to-cover, amounts), making the tool's function clear. It distinguishes this from sibling treasury tools by the 'auction results' focus, though it uses a noun phrase rather than an explicit verb like 'retrieves' or 'lists'.
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 explicit guidance on when to choose this tool over alternatives such as treasury_interest_rates, treasury_debt, or treasury_cash_balance. The optional security_type filter is mentioned, but there is no statement of default behavior or when this tool is or is not appropriate.
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.