Get Eurostat GDP (annual, million EUR, current prices) for an EU country or aggregate (DE, FR, IT, EU27_2020, EA20). Source: Eurostat dissemination API (free of charge, commercial use with attribution).
| Name | Required | Description | Default |
|---|---|---|---|
| geo | No | EU geo code (e.g. DE, FR, IT, EU27_2020, EA20). Default: DE |
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?
No annotations are provided, so the description carries the full burden. It discloses the data source and licensing (Eurostat API, free with attribution), which is useful. However, it does not describe the response format (e.g., single value vs. time series), rate limits, or behavior for invalid geo codes. More behavioral detail would improve transparency.
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 two concise sentences: the first states the core functionality with specific details, the second adds source and licensing info. Every word earns its place, and the most important 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?
Given the tool's simplicity (one optional parameter) and full schema coverage, the description is mostly complete. However, there is no output schema, and the description does not clarify whether the tool returns a single year's value or a historical series. This ambiguity prevents a higher score.
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 description fully covers the single 'geo' parameter, including examples and default. The tool description repeats these examples without adding additional meaning. With 100% schema coverage, the baseline is 3, and the description does not exceed that.
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 tool retrieves Eurostat GDP data, specifying the metric ('annual, million EUR, current prices') and geographic scope ('EU country or aggregate'). It distinguishes itself from sibling tools like eurostat_inflation and eurostat_unemployment by naming the exact data type, and from generic GDP tools by sourcing from Eurostat.
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 on when to use this tool: for annual GDP figures in million EUR at current prices for EU countries/aggregates. It does not explicitly mention alternatives or exclusions, but the specificity of the scope makes the usage context reasonably clear.
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.