NexusForge EU Finance

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by nexusforge-tools

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Server Quality Checklist

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Latest release: v1.0.5
Disambiguation5/5
Each tool has a clearly distinct purpose. The `compare_eu_economies` tool is designed for multi-country, multi-indicator comparisons, while the individual getter tools allow for more detailed, single-indicator queries. Their descriptions clearly delineate when to use each, preventing confusion.
Naming Consistency5/5
All tool names follow a consistent `verb_noun` pattern using lowercase and underscores (e.g., `compare_eu_economies`, `get_ecb_rates`, `get_eu_gdp`). The verbs are specific and the nouns precisely describe the resource, making the naming predictable and easy to understand.
Tool Count5/5
With 6 tools, the set is well-scoped for a server focused on EU economic data and exchange rates. It provides sufficient functionality without being overwhelming, covering the core indicators requested by users interested in European finance.
Completeness4/5
The tool set covers the major EU economic indicators (inflation, GDP, unemployment, ECB rates, exchange rates) and offers a convenient multi-indicator comparison. Minor gaps exist, such as the absence of tools for other ECB policy tools or more granular GDP components, but the essential lifecycle is covered for its stated purpose.
Average 4.7/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds significant behavioral context beyond annotations: caching delays (1 hour for latest, permanent for historical), non-real-time nature (published ~16:00 CET), weekend/holiday fallback, and date range constraints. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured: starts with purpose, then output format, caching, usage, limitations. Informative but slightly verbose; could be tightened without losing substance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully explains the return JSON structure, parameters, caching, and edge cases. Complete for a tool with 2 optional parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and description adds meaning: explains 'currencies' is a list of 3-letter codes (omit for all) and 'date' format/range, plus behavior when omitted. Adds value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches EUR exchange rates from the ECB via Frankfurter API, specifying the source and output structure. It distinguishes itself from sibling tools by focusing on exchange rates, but does not explicitly differentiate from 'get_ecb_rates' which might overlap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage guidance: omit 'date' for latest rates, provide YYYY-MM-DD for historical, lists supported currencies, and explains caching behavior. However, it lacks explicit when-not-to-use guidance or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint etc. The description adds caching (24h), period formats, and release lag, which are useful beyond annotations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with front-loaded purpose and detailed sections. Slightly longer than minimal, but every sentence adds value—clear and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully explains the return format, null handling, caching, and usage context, making it complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and includes clear descriptions for both parameters. The description adds context like using 'EA20' for Eurozone and that indicators can be filtered, enhancing but not duplicating schema info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetches') and resource ('latest inflation, GDP growth, and unemployment for 2-10 EU countries'), and distinguishes from sibling tools by noting it replaces three separate calls, making purpose crystal clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: 'Prefer this tool over calling get_eu_inflation, get_eu_gdp, and get_eu_unemployment separately' and advice to handle null gracefully, directly addressing when and how to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds key behavioral traits: data is cached for 24 hours, missing periods are omitted (not returned as null), and typical Eurostat data lag is 60-90 days. This provides rich context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence stating the tool's purpose. It then covers output format, defaults, caching, and usage in a logical order. While it is detailed, every sentence serves a purpose; minor verbosity could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description thoroughly explains the returned data structure (array of objects with country, period, value, unit), plus source and retrieved_at fields. It covers all parameters, default behavior, caching, data lag, and missing period handling. Complete for a data retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the semantic meaning of each unit (e.g., CLV_PCH_SM for year-on-year growth) and giving examples of country codes (EA20, EU27_2020). It also clarifies defaults and parameter effects.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches quarterly GDP data for EU/Eurozone countries from Eurostat, specifying the dataset (namq_10_gdp). It distinguishes from siblings which cover other economic indicators like inflation, unemployment, and exchange rates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit guidance on when to use each unit parameter (e.g., use CLV_PCH_SM for cross-country growth comparisons, CP_MEUR for absolute GDP size). It also mentions typical data lag and suggests using more quarters for recession analysis. However, it does not directly compare with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: caching for 1 hour, meaning of the date field, and that rates only change every 6 weeks. Annotations already indicate read-only and idempotent traits; no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections, front-loads the purpose, and adds necessary context. Slightly verbose but each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully covers what the tool returns, caching behavior, usage notes, and relationship to sibling tools. Complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so the description does not need to add parameter meanings. Baseline is 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool fetches the three ECB key interest rates from the ECB Statistical Data Warehouse. It explicitly lists the returned rates and distinguishes from siblings like get_euro_exchange and get_eu_inflation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when to use (ECB policy rates), when not to use (not for real-time market rates or forex), and suggests using with inflation for real interest rate calculation. It also mentions the update frequency.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. Description adds significant behavioral details: data is cached for 24 hours, typical data lag is 30-45 days, and output structure is JSON with specific fields. No contradictions with annotations. Adds valuable context beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured: first sentence explains core function, then output format, defaults, caching, usage guidelines, and additional tips. Every sentence serves a purpose with no fluff. Length is appropriate given the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity and simple schema (2 parameters, no nested objects, no output schema), the description fully covers what an agent needs: data source, output structure, default behavior, caching, data lag, and usage recommendations. Complete enough for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters (countries and periods). Description adds value by explaining that omitting countries returns all EU members, and provides usage examples for EA and EU27_2020. Also mentions that periods=12 gives 12-month trend. While schema already covers defaults, description reinforces and adds context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it fetches HICP annual inflation rates for EU countries from Eurostat, specifying the dataset code. The verb 'Fetches' and resource 'HICP annual inflation rates' are specific, and it is distinct from sibling tools like get_ecb_rates or get_eu_gdp which cover different economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance on when to use: 'HICP is the EU-harmonised inflation standard used by the ECB for monetary policy — use it (not national CPI) for cross-country comparisons.' Also explains typical data lag, how to retrieve trends with periods=12, and recommends pairing with get_ecb_rates for context. Clearly states when to use vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, destructiveHint, idempotentHint, openWorldHint. The description adds valuable behavioral context: data cached 24 hours, typical lag 30-60 days, seasonally adjusted, and default parameters. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Approximately 10 sentences, well-structured: begins with core purpose, then output format, defaults, caching, and usage advice. Every sentence adds meaningful information without repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three parameters and no output schema, the description fully covers output format, source, caching, lag, and usage scenarios like youth unemployment and trend analysis. It leaves no critical gaps for an agent to understand proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all three parameters. The description enriches semantics by explaining default countries, providing usage tips for each parameter (e.g., using age for youth) and clarifying that countries list can include aggregates like EA20. It adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches monthly unemployment rates for EU countries from Eurostat (dataset: une_rt_m), seasonally adjusted, and specifies the output structure. It distinguishes from siblings by noting it is only for EU member states and EA20/EU27_2020 aggregates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: when to use age=Y15-24 for youth unemployment, setting months=12+ for trend analysis, and warns that non-EU countries are not available. It also notes the data is seasonally adjusted so no additional correction is needed, and mentions typical Eurostat lag.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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