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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds a valuable operational caveat: unknown country codes yield HTTP 200 with an empty result list and explanatory hints, preventing typo/empty-data confusion. It does not cover rate limits or auth requirements.

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?

Well-structured and front-loaded: purpose first, then use cases, a behavioral caveat, and an Args/Returns summary. The four example bullets are slightly verbose, but each illustrates a distinct usage pattern, so they largely earn their place.

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?

For a read-only data-fetch tool with an output schema and full annotations, the description covers purpose, usage examples, a key caveat, and output format. It omits explicit sibling routing and detailed year-range semantics, but is otherwise adequate for correct invocation.

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?

With reported schema description coverage at 0%, the description lists the parameters (indicator_id required, country_code, start_year, end_year) and examples clarify indicator IDs and country usage. But it adds little semantic detail beyond names and examples already present in the schema's own property descriptions.

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?

States a specific verb and resource: retrieves education data from the UNESCO UIS API, with scope of a specific indicator for one or all countries. It calls itself the server's core function and gives concrete indicator examples, but does not explicitly distinguish itself from siblings like uis_compare_countries or uis_country_education_profile.

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?

Provides four typical use-case examples with concrete indicator/country combinations, which implies when to use it. However, it never states when not to use it or names alternative sibling tools, so the routing guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare this as a read-only, idempotent, open-world, non-destructive operation. The description adds useful behavioral context: it explains that the output is a Markdown list of ISO codes and country names, and it distinguishes between NATIONAL and REGIONAL entities, including examples of regional aggregates. It does not cover auth requirements or rate limits, but the annotations carry the safety profile.

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 front-loaded with purpose and return value, then adds practical examples and entity-type context. The Args and Returns sections are compact, though the Args line slightly conflates nesting and direct arguments. Overall it is efficient with little wasted text.

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?

An output schema exists, so the description need not document return values, yet it helpfully summarizes the Markdown list format. Annotations cover the safety profile, and the description covers entity types, filtering, and the purpose of the returned codes. A minor gap is the lack of explicit nesting guidance for the params object.

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 itself provides detailed descriptions for the nested search and entity_type properties, including allowed entity_type values and caveats about unsupported values. The tool description adds marginal value by naming both optional filters and giving examples of regional aggregate types, but it misleadingly presents search and entity_type as direct arguments rather than as fields nested inside the required params object. Given the schema already does the heavy lifting, this is adequate but imperfect.

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 states a specific verb and resource: it lists available countries and regions in the UNESCO UIS database. It also clarifies that the tool returns ISO 3166-1 alpha-3 codes needed for data queries, which distinguishes it from indicator-listing or data-retrieval siblings. However, it does not explicitly name or contrast itself with any sibling tool, so it falls short of the highest clarity tier.

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 by saying the returned ISO codes are needed for data queries, and it mentions optional filtering via search and entity_type. It does not state when to use this tool versus alternatives such as uis_list_indicators or uis_compare_countries, nor does it provide any when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnlyHint, idempotentHint, openWorldHint and destructiveHint, so safety is handled. The description adds real value beyond that: the scale of the catalog, the concrete indicator-prefix categories, and notably a warning that NERA.*, XUNIT.* and PTR.* prefixes do not exist at the source, which prevents wasted queries. It does not cover pagination 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first line, followed by scope, then the category reference block. The category list is long but earns its place as search fodder; the parenthetical about which prefixes were removed from the list is slightly noisy bookkeeping an agent does not strictly need.

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?

Safety traits come from annotations and the return shape is covered by an output schema plus a one-line mention of a Markdown indicator list, so the description need not explain returns. For a single-parameter, read-only catalog tool this is essentially complete, with only filter behaviour (how theme and search combine) left unspecified.

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?

Reported schema description coverage is 0% at the top level (the single 'params' object carries no description), so the description partially compensates by enumerating theme, search and limit and marking the first two optional. It adds no syntax, format or value guidance beyond what the nested property descriptions already state, so it is minimum-viable rather than enriching.

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 opening sentence gives a specific verb and resource (list available UIS indicators) and the follow-up quantifies scope (over 4,000 indicators across education, science, culture, communication). An agent can distinguish this exploration/catalog tool from siblings like uis_get_education_data or uis_compare_countries without opening the schema.

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?

'Diese Funktion dient zur Exploration und Indikatorsuche' implies the when-to-use case (discovering indicator IDs before fetching data), which is a reasonable implicit signal. However, it never names an alternative tool or states when not to use it, so the routing decision against uis_get_education_data is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.