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). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior, so the description does not need to repeat those. It adds meaningful context beyond the annotations: the data source, the fact that both the most recent figure and history are returned, the split between public debt and intragovernmental holdings, and the keyless access.
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 tight sentences convey the subject, source, return content, breakdown, authentication need, and data authority. There is no filler, and the most identifying information is front-loaded.
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 data tool with fully documented optional parameters and strong annotations, the description is nearly complete. It tells the agent what data is returned and that no key is needed, though it could have briefly noted date-range/limit behavior or explicitly distinguished itself from the other treasury_* siblings.
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%, so the parameter meanings are already documented. The description adds only a generic sense that history and a latest figure are available, but it does not clarify how limit/start_date/end_date interact or what defaults apply. 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 names a specific resource ('US total public debt outstanding', the 'Debt to the Penny' series), a clear action ('Returns...'), and the data breakdown (public versus intragovernmental holdings). This is enough to distinguish it from sibling treasury tools like treasury_cash_balance or treasury_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 intended use is implied by the explicit resource description, and the 'Keyless, official Treasury data' note is a useful usage affordance. However, there is no explicit guidance on when to choose this tool over related Treasury siblings, nor any exclusions or alternative recommendations.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.