US total public debt outstanding (the 'Debt to the Penny' series from the US Treasury). Returns the most recent figure plus history, split into debt held by the public and intragovernmental holdings. Keyless, official Treasury data.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. | |
| end_date | No | Latest record date (YYYY-MM-DD). | |
| start_date | No | Earliest record date (YYYY-MM-DD). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only/idempotent/non-destructive behavior; the description adds valuable extra context by noting no API key is needed and by specifying that results include both the most recent figure and history broken down by holder category. No annotation contradiction.
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 focused sentences: the first names the dataset and source, the second states what is returned. No filler or redundant restatement.
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 keyless read-only tool with optional parameters, the description supplies the source, auth need, and return composition, while the schema covers query parameters. It could add output format or cadence details, but nothing essential is missing for an agent to select and call it.
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?
All three parameters are already fully described in the JSON schema, so the baseline applies. The description's mention of 'history' and 'most recent figure' aligns with start_date/end_date/limit but does not add new parameter-level detail.
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 opens with a specific resource ('US total public debt outstanding'), cites the exact Treasury series ('Debt to the Penny'), and states the return shape, so an agent can clearly distinguish it from other treasury_* tools such as treasury_interest_rates or treasury_cash_balance.
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 communicates the right context: this is the keyless, official Treasury source for total debt and provides both latest and historical figures. It does not explicitly name alternatives or exclusions, so it stops short of a 5.
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.