global-education-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.3.5
Disambiguation5/5
Each tool targets a distinct operation: listing, searching, retrieving raw data, comparing countries, generating benchmarks, or creating profiles. The only potential overlap (education_benchmark_countries vs uis_compare_countries) is actually complementary (multi-indicator benchmark vs single-indicator comparison), so no ambiguity.
Naming Consistency5/5
All tools follow a consistent <source>_<verb>_<noun> pattern in snake_case (e.g., uis_list_indicators, oecd_get_education_indicator). The prefix indicates the data source (education_, oecd_, uis_), making it easy to navigate.
Tool Count5/5
With 10 tools, the server covers two major data sources (UNESCO UIS and OECD) across essential operations: discovery, retrieval, comparison, benchmarking, and country profiling. This is a well-scoped set that avoids bloat while providing sufficient functionality.
Completeness5/5
The tool surface covers the full lifecycle of accessing education statistics: listing available indicators/countries/versions, searching, fetching raw data, comparing countries, creating profiles, and running multi-country benchmarks. No obvious gaps for a read-only data API.
Average 3.9/5 across 10 of 10 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 33 commits in the last 12 weeks
- Last stable release on July 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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}
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is clearly non-destructive. The description adds context about accessing the OECD API and returning structured data, but does not go beyond what annotations imply. No contradictions are present.
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 reasonably concise and well-structured, with a clear introduction, helpful examples, and a brief listing of arguments. It front-loads the main purpose and uses bullet points for examples. It could be slightly shorter without losing key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the existence of an output schema (though not provided here), the description adequately covers the return format as markdown table or summary. It also mentions available dataflow IDs. However, it omits potential error conditions or dataset size warnings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides detailed descriptions for each parameter, so the description adds limited value by simply listing parameter names. The description mentions 'dataflow_id' and 'countries' but does not elaborate on their meaning beyond what the schema already provides. With high schema coverage, a baseline of 3 is appropriate.
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 that the tool retrieves education data from OECD Education at a Glance report via SDMX REST API. It provides specific examples of dataflows and countries, making the purpose evident. However, it does not explicitly differentiate from sibling tools like uis_get_education_data, which serve similar but distinct data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers examples of usage but lacks explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or scenarios where sibling tools might be more appropriate. This omission reduces clarity for an AI agent deciding which tool to invoke.
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?
The description adds that it returns a Markdown comparison table sorted by indicator value, which is useful beyond the annotations (readOnlyHint, idempotentHint). It does not contradict any annotation.
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 concise (3 sentences plus args/return) and front-loaded with the main purpose. It could be more structured (e.g., bulleted info) but is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (though not shown) and the complexity of the tool (comparison with country list), the description adequately covers input, output format, and use case. It lacks error handling details but is sufficient for selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists the parameters (indicator_id, country_codes, year) with minimal additional context (e.g., year optional, country codes as list). The schema already provides detailed descriptions for each field, so the description adds limited value.
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 the tool compares educational indicators across countries using UNESCO UIS data, and provides an example (Switzerland vs Finland etc.). It is specific and actionable, but does not explicitly differentiate from sibling tools like education_benchmark_countries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Ideal für den direkten internationalen Vergleich' suggests when to use, but there is no guidance on when not to use or mention of alternative tools (e.g., uis_get_education_data for single country queries).
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=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds value by noting the API source ('OECD SDMX API'), that it searches hundreds of datasets, and that the output is a Markdown list with Dataflow-IDs. No contradictions 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?
The description is well-structured with a lead sentence stating the purpose, followed by context, then an Args/Returns section. It is concise and contains no superfluous information, earning a high score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters, output as list), the description covers purpose, data source, and output format. An output schema exists, so explaining return values is unnecessary. Missing details like error handling or empty results are minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description barely adds value beyond the input schema: it only lists parameter names ('keyword, limit') without describing their purpose or constraints. The schema itself already contains good descriptions for each parameter, so the description's contribution is minimal, leading to a low score.
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 'Durchsucht alle OECD-Datensätze nach einem Stichwort', identifying the verb (search) and resource (all OECD datasets) with a specific action (by keyword). This distinguishes it from siblings like oecd_list_education_datasets, which likely lists all without searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context about the SDMX API and that it finds datasets related to education or other topics, but it does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites or when not to use it, leaving differentiation to the agent's inference.
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 convey read-only, idempotent, and open-world nature. The description adds value by specifying the return format (Markdown list with descriptions and dataflow IDs) and the scope (OECD countries plus partners), which goes beyond the 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 bullet points and clear sections. It front-loads the main purpose. Slightly verbose in the topic area listing but overall concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, an output schema exists, and rich annotations, the description covers purpose and return value well. However, it lacks guidance on when to use relative to siblings, which slightly detracts from completeness.
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 tool has zero parameters, and the schema coverage is 100% (trivially). The description does not need to add parameter semantics, and the baseline for 0 parameters is 4. It doesn't detract from this score.
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 it lists available OECD Education at a Glance datasets, specifying the resource and action. It enumerates topic areas with example dataflow IDs, effectively distinguishing it from sibling tools like oecd_get_education_indicator or uis_list_countries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives. No context about prerequisites or exclusions, such as when a more specific tool like oecd_search_datasets might be appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. Description adds that it returns a Markdown profile with specific indicator types, but no additional behavioral details (e.g., no mention of side effects, auth, or rate limits).
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?
Concise with short bullet list of indicators, clear Args and Returns sections. Efficient use of space, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description adequately covers the tool's purpose and return format (Markdown profile). No major gaps, though edge cases like missing data could be addressed.
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 descriptions already cover parameter details (country_code format, latest_year_only toggle). Description adds value by listing the types of indicators included (literacy, enrollment, etc.), which aids understanding beyond 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 it creates a comprehensive education profile for a country using UNESCO UIS, listing specific indicators. It distinguishes from sibling tools like uis_get_education_data (raw data) and uis_compare_countries (comparison).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for obtaining a full country profile, but no explicit guidance on when to use vs alternatives (e.g., uis_get_education_data for raw data). No when-not-to-use or prerequisite conditions.
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=true, destructiveHint=false, idempotentHint=true. The description adds that the result is a markdown-formatted table or time series, and warns that omitting a country can return a large dataset. This provides useful behavioral 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 purpose, examples, and parameter list. It is moderately sized and front-loaded. Some redundancy with the schema exists, but overall it is efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (nested parameters, many sibling tools), the description covers purpose, usage examples, parameter list, and return format. It references sibling tools indirectly in the parameter description. With annotations and output schema present, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the input schema has detailed descriptions inside $defs, the top-level schema coverage is 0%. The description lists parameters and gives examples, but does not explain each parameter in depth. It partially compensates for the low schema coverage but not fully.
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 retrieves educational data from the UNESCO Institute for Statistics API, specifying it is the core function of the server and providing concrete examples with expected outputs. It distinguishes itself from siblings by focusing on raw indicator data for one or all countries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives typical use cases with example parameters, but does not explicitly tell when to avoid this tool in favor of siblings like uis_compare_countries or uis_list_indicators. The context of usage is implied but lacks explicit exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds behavioral context by explaining it's for exploration and listing categories, but does not disclose additional traits like pagination or rate limits. 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?
The description is well-structured: starts with purpose, then categories, then simple args/returns. Every sentence is informative. Could be slightly more concise (e.g., the args section is minimal), but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (thousands of indicators with theme/search), the description provides adequate categories and filter options. The output is described as a Markdown list with IDs and descriptions, which aligns with the exploration purpose. No output schema was provided, but the description covers key aspects.
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 descriptions cover all parameters (theme, search, limit) with clear details, so the description need not repeat them. However, the description adds value by listing example indicator categories (e.g., LR.*, NERA.*) that help the agent understand what to search for, going beyond the schema's formal descriptions.
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 specifies 'listet verfügbare Indikatoren' (lists available indicators) from UNESCO Institute for Statistics, with a scope of over 4,000 indicators across education, science, culture. It distinguishes from sibling tools like uis_get_education_data (which fetches data) by focusing on exploration and search.
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 explicitly states the function is for 'Exploration und Indikatorsuche' (exploration and indicator search), providing categories to guide use. It doesn't explicitly list when not to use or contrast with siblings, but the categories and purpose imply the correct context.
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 mark the tool as read-only and non-destructive. The description adds behavioral context: it automatically selects appropriate indicators based on focus and generates a structured report. This goes beyond annotations but does not detail other potential side effects.
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?
The description is concise and well-structured, using bullet points for focus options. It front-loads the core purpose and then details parameters and output efficiently. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and annotations, the description covers purpose, parameters, and output format. It lacks discussion of error handling or prerequisites but is adequate for a non-destructive benchmarking 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?
Despite schema description coverage reported as 0%, the description compensates by explaining the focus parameter with detailed options and linking to the required country_codes parameter. It adds meaning beyond the raw schema by providing context for each focus area.
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 benchmarks multiple countries on a specific education topic using UNESCO UIS. It lists five distinct focus areas and contrasts with siblings by emphasizing automated indicator selection for structured comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for benchmarking on specific topics but does not explicitly state when to use this tool versus alternatives like uis_compare_countries. No 'when-not-to-use' guidance or references to sibling tools are provided.
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, destructiveHint, idempotentHint. The description adds that it returns ISO codes and provides examples, plus the Args/Returns section. No contradictions, and it adds useful 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loads the main purpose, includes examples, and is well-structured with paragraphs and an Args/Returns block. No unnecessary information.
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 presence of an output schema and low tool complexity, the description provides sufficient context: purpose, return format, examples, and types of regions. It is complete for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already contains descriptions for both parameters (search and entity_type). The description briefly repeats them in the Args section but does not add significant new meaning. Schema description coverage is 0% according to signal, but actual schema descriptions exist, so the description adds minimal value.
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's purpose: listing available countries and regions from UNESCO UIS database, returning ISO codes. It distinguishes from sibling tools like uis_compare_countries and uis_get_education_data by focusing on the list of regions.
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 explains that the tool is used to obtain ISO codes for data queries and lists various region types. It does not explicitly state when not to use, but the context is clear and sufficient.
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 provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds value by explaining that UIS publishes multiple new versions per year and that the tool returns a Markdown list with version names and publication dates, which is beyond the 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?
The description is very concise with only three sentences, front-loaded with the core action. Every sentence adds value: stating function, providing context about publication frequency, and specifying the return format. No unnecessary text.
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 a simple listing task, the description is complete. It explains the purpose, the context of version releases, and the return format (Markdown list). Annotations cover safety and idempotency, and an output schema exists. No gaps remain.
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?
No parameters exist, so schema coverage is 100% and baseline is 4. The description does not add parameter information because none are needed. This is appropriate.
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 lists available versions of the UNESCO UIS database, distinguishing it from sibling tools like uis_list_countries and uis_list_indicators. The verb 'listet' and specific resource 'Versionen der UNESCO UIS-Datenbank' provide clear purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is useful to ensure working with the latest data, implying usage context, but does not provide explicit when-to-use or when-not-to-use guidance or compare with alternatives. No exclusions are stated.
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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