Get a headline U.S. Treasury dashboard: national debt, interest rates, monthly statement, gold reserve, EUR rate. Source: U.S. Treasury Fiscal Data (public domain, no key).
| Name | Required | Description | Default |
|---|---|---|---|
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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?
With no annotations, the description carries the full transparency burden. It discloses that the data is sourced from U.S. Treasury Fiscal Data, is public domain, and requires no API key, which is useful operational context. However, it does not describe the response format or potential caveats like latency or error handling, but for a simple zero-parameter fetch, the provided information is reasonably transparent.
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 a single, front-loaded sentence that immediately states purpose and key content. The additional source/authentication note is necessary for usage. No word is wasted.
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?
Given the tool's simplicity (no parameters, no output schema), the description fully specifies what the tool returns: a dashboard with the listed treasury indicators. The source note also covers data origin and key requirements, making the description sufficient for an agent to select and invoke the tool.
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 tool has zero parameters, and the schema is empty with 100% description coverage (nothing to describe). Per the rubric, a baseline of 4 is appropriate since there is no parameter ambiguity to resolve.
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 states the verb 'Get' and the resource 'headline U.S. Treasury dashboard', enumerating specific components (national debt, interest rates, monthly statement, gold reserve, EUR rate). This distinguishes it from sibling tools like treasury_debt or treasury_interest_rates by presenting it as a cohesive summary rather than a single metric.
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 phrase 'headline dashboard' conveys a clear use case for obtaining a quick overview of U.S. Treasury data. It implies a high-level summary, making it distinct from the more granular sibling tools, though it does not explicitly name alternatives or state 'use this when you need a broad snapshot'.
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 groups have overlapping purposes: fda_drug_labels vs health_drug_search, fda_recalls vs health_recalls, fx_official_rates vs treasury_fx_rates, treasury_debt vs us_debt_current, and get_gdp vs get_bea_gdp. These near-duplicates create real ambiguity for an agent deciding which tool to call.
Names mix verb-led styles (get_, search_, compare_, screen_) with domain-led styles (fx_, treasury_, uk_, health_, eurostat_, datausa_). Within the same domain, similar actions use different patterns (get_gdp vs eurostat_gdp vs imf_indicator), making the set feel inconsistent and hard to predict.
75 tools is extreme for any MCP server, especially when many tools are redundant or cover unrelated domains (weather, earthquakes, scholarly search, air quality) outside the stated SEC/economics/demographics/FX focus. This overwhelms agents and burdens context windows.
Core domains like SEC financials, major economic indicators, basic demographics, and current FX rates are well covered. However, gaps remain: no historical FX rates, no stock price/quote tool, limited demographic breakdowns, and no ability to fetch full SEC filing text. Some operations end in dead ends.