Official US Treasury Reporting Rates of Exchange (the rates US government agencies use to convert foreign currency balances to dollars). Published quarterly. Provide a country or currency to filter, e.g. 'Canada', 'Euro', 'Yen'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. | |
| query | No | Country or currency name to match, e.g. 'Canada', 'Euro Zone', 'Japan'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds meaningful context beyond annotations: the data is published quarterly, is the official US government source for converting foreign balances to dollars, and supports country/currency filtering. It does not describe response format or pagination, but the safety profile is already known.
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 with no filler. The first identifies the authoritative purpose and cadence; the second gives direct usage instruction with three quick examples. Every sentence earns its place.
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, two-parameter tool, the description covers source, purpose, freshness, and query format. It does not state behavior when no query is provided or describe the return structure, but the schema handles limit and the examples suffice for correct invocation.
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 parameters query and limit are already documented. The description adds example values like 'Canada', 'Euro', and 'Yen', which slightly reinforces the query parameter semantics but does not explain limit behavior or provide meaning beyond the schema. 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?
The description clearly identifies the resource as official US Treasury exchange rates used for converting foreign currency balances to dollars, which distinguishes it from sibling tools like treasury_interest_rates. It lacks an explicit retrieval verb such as 'get' or 'return,' but the phrase 'Reporting Rates of Exchange' and the filter instruction make the operation clear.
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 provides clear context: this is the official Treasury rate source, published quarterly, and users should provide a country or currency to filter. It does not explicitly name alternatives or state when not to use the tool, but the domain and filter examples effectively communicate usage.
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.