Get EU economic statistics from Eurostat. Covers GDP, inflation (HICP), unemployment rates, and trade data for EU member states.
| Name | Required | Description | Default |
|---|---|---|---|
| countries | No | Comma-separated EU country codes (default: DE,FR,IT,ES,NL,PL,BE,SE,AT,DK) | DE,FR,IT,ES,NL,PL,BE,SE,AT,DK |
| indicator | No | gdp | inflation | unemployment | trade (default: gdp) | gdp |
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 must convey behavioral traits. It states the data types covered (GDP, inflation, etc.) but omits details like data freshness, API limits, whether it is read-only, or error handling. The name 'get' suggests a read operation, but additional context is lacking.
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 sentences with no redundant words. It efficiently communicates the tool's purpose and scope, front-loading key 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 has no output schema, so the description should explain the return structure (e.g., format, fields, time periods). It does not, leaving the agent without essential information for interpreting results. Given the simplicity, this is a notable gap.
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?
Both parameters have schema descriptions that cover their purpose (country codes and indicator options). The description adds a list of indicators but does not provide new semantic details beyond the schema. With 100% schema coverage, the baseline is 3, and the description does not elevate it.
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 'EU economic statistics from Eurostat', and lists specific indicators (GDP, inflation, unemployment, trade) and scope (EU member states). It implicitly distinguishes from sibling tools like get_ecb_rates and get_money_supply which cover different economic domains.
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 implies usage for EU economic data but does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context-specific guidance, leaving the agent to infer use cases.
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.
Each tool targets a distinct category of European economic data: ECB exchange rates and interest rates, Eurostat statistics, and ECB monetary data. Descriptions clearly differentiate them, preventing confusion.
All three tools use a consistent 'get_' prefix followed by descriptive snake_case names (e.g., get_ecb_rates, get_eurostat_data), forming a predictable pattern.
With only 3 tools, the server is tightly scoped to European economic data. Each tool serves a clear purpose without unnecessary bloat, making it easy for agents to navigate.
The tool set covers key areas: exchange rates, macroeconomic indicators (GDP, inflation, unemployment), and monetary aggregates. Minor gaps exist (e.g., no tool for bond yields or historical exchange rate ranges), but core needs are met.