European economic intelligence — ECB EUR/USD exchange rates, Eurozone GDP/inflation/unemployment via Eurostat, and M3 money supply data. First EU-specific x402 data API.
Scored across 3 tools
Each tool targets a distinct data domain: exchange rates/interest rates, Eurostat macro statistics, and monetary aggregates. No overlap in purpose or data source, so an agent would not confuse them.
All three tools follow the exact same get_<source_or_topic> pattern (get_ecb_rates, get_eurostat_data, get_money_supply). Naming is uniform and predictable.
Three tools is well-scoped for a focused European economic data server. Each tool is non-trivial and covers a major economic data category without bloat.
The server covers key European economic indicators: rates, macro statistics, and monetary data. It could be expanded with e.g. bond yields or forecasts, but the core data needs are well covered.
Get ECB exchange rates for EUR against major currencies, or ECB key interest rates (ESTR). Returns live rates from the European Central Bank Statistical Data Warehouse.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | fx (exchange rates, default) or rates (interest rates) | fx |
| currencies | No | Comma-separated currency codes (default: USD,GBP,JPY,CHF,CNY) | USD,GBP,JPY,CHF,CNY |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Mentions 'live rates' and the data source but does not elaborate on behavioral traits such as authentication needs, rate limits, or whether the operation is safe (though reading is implied). With no annotations, the description carries the burden but only minimally addresses it.
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?
Single sentence covering the tool's purpose and data source. Concise and to the point, though could be slightly more structured with separation of modes.
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?
With no output schema, the description does not explain the return format or structure of the rates. While it covers the two modes and default currencies, it omits important context like the base currency (EUR) being fixed and the data source details.
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%, with both parameters already explained in the schema. The description merely repeats the schema text, adding no additional semantic value beyond what the schema provides.
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?
Clearly states it gets ECB exchange rates for EUR against major currencies or key interest rates, specifying the source (ECB Statistical Data Warehouse). Distinguishes from sibling tools which are different data sources.
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?
Implies usage for fetching ECB rates but provides no explicit guidance on when to use versus alternatives like get_eurostat_data or get_money_supply, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 |
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.
Get ECB monetary data. M3 money supply for the Euro Area or TARGET2 interbank balance data. Returns the last 12 monthly observations.
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | m3 (M3 money supply, default) or target2 (TARGET2 balances) | m3 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry all behavioral info. It discloses that the tool returns the last 12 monthly observations and can return two data types. However, it omits details like whether the operation is read-only (implied but not stated), error conditions, or data freshness. Adequate but not rich.
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 sentences with no redundant words. The first sentence immediately states the purpose, and the second provides specifics. Perfectly concise and 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?
The tool is simple (one parameter, no output schema), but the description only partially explains the return format ('last 12 monthly observations'). Without an output schema, more detail on data structure (e.g., date format, value type) would improve completeness. Adequate for a simple tool but leaves ambiguity.
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 coverage is 100% with parameter descriptions already explaining 'm3' and 'target2'. The description reiterates these values without adding new meaning (e.g., units, date range). Baseline score applies as no additional semantics are provided.
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 it retrieves ECB monetary data, specifying two distinct series (M3 money supply and TARGET2 balances). This directly differentiates it from sibling tools get_ecb_rates (interest rates) and get_eurostat_data (broader statistics).
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 monetary data but does not explicitly guide when to use this tool over the siblings. It lacks 'when-to-use' or 'when-not-to-use' statements, though the specific mention of 'monetary data' provides some context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_ecb_ratesget_eurostat_dataget_money_supply
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