Daily operating cash balance of the US Treasury (the Treasury General Account, the government's checking account at the Fed), from the Daily Treasury Statement. Values are in millions of dollars.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful provenance (Daily Treasury Statement) and unit clarification (millions of dollars), but does not describe the shape of the returned data, ordering, or how the limit parameter affects results. There is no contradiction with annotations.
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 one focused sentence with no filler. It front-loads the most important qualifier ('Daily'), then adds the specific account identity, source, and units, with every clause contributing useful information.
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?
The tool is simple and well-annotated as read-only, but there is no output schema, so the description carries the burden of explaining what the response represents. It identifies the series and units, yet does not say whether it returns a historical time series, just the latest value, or how `limit` shapes the response. These gaps are modest for a one-parameter lookup, but they are 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 covers the single optional `limit` parameter at 100% with 'Max rows to return,' so the baseline is 3. The description adds nothing about parameter behavior or defaults, but none is strictly required given the schema already documents it adequately.
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 precisely identifies the resource: the US Treasury's daily operating cash balance, clarifying it is the Treasury General Account at the Fed and sourced from the Daily Treasury Statement. This makes it clearly distinct from sibling treasury_* tools such as treasury_debt, treasury_interest_rates, and treasury_auctions. However, it lacks an explicit verb like 'returns' or 'retrieves,' so it falls just short of a 5.
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 does not state when to use this tool versus other treasury_* alternatives, nor does it give exclusions or prerequisites. Its usage is only implied by the title and noun-phrase description, which is weak guidance for an agent deciding among many sibling data tools.
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.