Statistics of the World

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

No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, what the output format is, how defaults are selected, or edge-case behavior. The only behavioral detail is the 2-10 country limit, which is also in the schema. This is insufficient.

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 a single, front-loaded sentence with no wasted words, effectively stating the tool's core action.

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 absence of annotations and output schema, the description is too minimal to provide complete context. It does not explain what the comparison result looks like, how data is sourced, or any caveats. Sibling tools like get_indicator_ranking suggest different purposes, so more detail is needed.

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%, so the baseline is 3. The description adds no new parameter details beyond the schema, except clarifying the minimum of 2 countries, but this is minor. The schema already documents both params.

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 uses a specific verb (Compare) and resource (countries on indicators), and the name differentiates it from sibling compare_sources. It also specifies the count range (2-10), making the tool's scope clear.

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?

No explicit guidance on when to use versus alternatives; the description only states the action. However, the tool's name and context imply it is for multi-country comparisons, while siblings like get_country_overview target single countries. Since this is not explicit, score 3.

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?

With no annotations, the description carries the burden and discloses that sources legitimately disagree due to vintages, exchange-rate conventions, and revision cycles. It explicitly says the tool surfaces these disagreements rather than hiding them, which is valuable behavioral context.

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, front-loaded with the core purpose, and each sentence earns its place—purpose, return structure, and scope condition. No unnecessary 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?

It fully explains what is returned (values per source with year), the scope (per country), and the limitation (multi-source mapping concepts). With no output schema and only 2 parameters, the description is sufficiently complete for an agent to invoke the tool correctly.

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 already provides 100% coverage with descriptions and examples for both parameters. The description adds no additional parameter-level detail beyond the schema, so the 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 identifies the tool as doing cross-source reconciliation, returning IMF, World Bank, and UN values side-by-side per country. It distinguishes itself from siblings like compare_countries or get_historical_data by focusing on comparing data sources rather than countries or single series.

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?

It states the tool works for concepts with a multi-source mapping, which implies when to use it. It does not explicitly name alternatives or state when not to use it, but the context of cross-source comparison is clear.

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?

No annotations are present, so the description carries the burden. It discloses that results are grouped by category, which is useful, but it does not mention permissions, response format details, or pagination. The read-only nature is implied but not stated.

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 a single, front-loaded sentence that efficiently conveys the core action and purpose with no unnecessary words.

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 simple list tool with no output schema, the description adequately conveys that it returns categories and their indicators. It lacks finer details like filtering semantics or potential size, but the parameter description covers the basics, making this sufficient for its simple nature.

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 already provides 100% coverage for the optional category parameter. The description adds 'grouped by category', which infers how the filter relates to the output structure, offering a slight enhancement beyond the 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 uses a specific verb 'List' and identifies the resource 'available indicators', adding 'grouped by category' to clarify the output structure. This distinguishes it from sibling tools like search_indicators.

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 mention when to use this tool versus alternatives such as search_indicators. It only states what it does, leaving the agent without explicit guidance on tool selection.

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?

In the absence of annotations, the description carries the burden of transparency. It discloses the exact number of countries (218) and the metadata fields (region, income level, capital), setting expectations for the response. The operation is inherently read-only (a list), so no side effects are implied. However, it doesn't mention auth requirements or response format, but for a simple list this is sufficient.

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 a single sentence that is front-loaded with the action ('List') and the resource ('all 218 countries'). It includes metadata details without any fluff. Every word earns its 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?

Given the simplicity of the tool (one optional parameter, no output schema), the description is fairly complete. It clearly states the output scope and the included metadata fields, and the behavior with and without the region filter is implied. It doesn't describe the exact response structure, but for a list operation this is not a critical 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?

The schema already documents the single optional parameter 'region' with 'Optional: filter by region' at 100% coverage. The description does not add further meaning to this parameter beyond what the schema provides, so a baseline score 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 the tool's purpose: 'List all 218 countries with metadata (region, income level, capital).' The verb 'list' is specific, the resource is 'countries', and the scope ('all 218') is explicit. This distinguishes it from sibling tools like compare_countries or get_country_overview, which serve different purposes.

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 obtaining a full list of countries, but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusion criteria. For example, it doesn't say 'use get_country_overview for detailed single-country info.' This is implied but not explicitly guided.

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?

With no annotations provided, the description carries the burden. It discloses the general behavior (returns multiple statistics) and lists fields, but does not mention time periods, data freshness, or any limitations. This is 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?

One sentence, front-loaded with the action and resource, and uses an example list. No wasted words; it is concise and well-structured.

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 simple one-parameter tool with no output schema, the description covers the core purpose and return fields. However, it could be improved by specifying whether data reflects current year or a specific timeframe, and it does not reference sibling tools.

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 fully documents the only parameter (country_id) with a detailed description of ISO codes, so the tool description adds no additional parameter semantics. The high schema coverage justifies the baseline 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 the tool retrieves key statistics for a country and lists specific examples (GDP, population, life expectancy). It does not explicitly distinguish from sibling tools like get_historical_data or compare_countries, so it misses the top score.

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 when a country overview is needed, but provides no explicit guidance on when to use it versus alternatives such as get_historical_data or get_series_data. This is implied usage, not explicit direction.

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?

With no annotations provided, the description carries full responsibility for behavioral disclosure. It adds the '20+ years' time span detail, but it does not explicitly state that the operation is read-only, does not mention response format, pagination, authentication needs, or error behavior. The verb 'get' implies a safe read, but the lack of explicit disclosure leaves significant gaps.

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 a single sentence, front-loaded with the core action and scope, and contains no redundant or filler text. Every word adds value, and it is highly scannable.

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 relatively simple with only two parameters and a clear purpose, and the description covers the main functionality. However, it does not explain the return structure of the time series, nor does it clarify how to obtain valid indicator_id or country_id values, and it does not address potential overlap with get_series_data. These omissions leave some operational 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 description coverage is 100% (both parameters have descriptive comments). The description adds no new parameter-level semantics beyond what the schema already provides—it merely restates 'country and indicator pair,' which maps directly to the two parameters. Per the calibration baseline, a score of 3 is appropriate when the schema already documents all parameters.

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 a specific verb ('Get'), a resource ('historical time series'), and a precise scope ('country and indicator pair', '20+ years'). This effectively distinguishes it from sibling tools like get_series_data by emphasizing the long time span, while also being immediately understandable.

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 conveys clear context: use this tool to retrieve historical time series for a specific country and indicator over 20+ years. It implies the appropriate scenario, though it does not explicitly mention alternative tools or any when-not-to-use 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?

No annotations are provided, so the description carries the burden. It discloses tier-based availability and top/bottom support, but does not explain return format, missing data behavior, or potential errors.

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 clear front-loaded purpose, no redundancy.

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 description gives a basic overview but lacks output format details and edge-case behavior, leaving the agent uncertain about the response structure.

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?

All three parameters are well-described in the schema, and the description adds nothing beyond the schema. Baseline 3 applies due to high schema coverage.

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 a specific action (rank countries) and resource (indicator available to the caller's tier), distinguishing it from sibling tools like get_historical_data or search_indicators by focusing on ranking/ordering.

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 ranking queries but does not explicitly state when to prefer this over alternatives like get_historical_data or compare_countries, nor does it mention exclusions.

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?

With no annotations to rely on, the description carries the burden and does a solid job: it warns that a passed date does not confirm publication and that related series readings are not verified against the scheduled release. This is meaningful behavioral context beyond the schema, though it does not cover error handling 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?

The description is two sentences with no filler. The core subject is front-loaded in the first sentence, and the second sentence packs critical caveats compactly without being verbose.

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-oriented calendar tool with fully described optional parameters, the description conveys the main output type, mentions primary source URLs, and provides the key reliability caveats. It is slightly less complete because there is no output schema and the return shape is not described beyond the basic calendar concept.

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%, so the schema already documents all six parameters. The description adds contextual nuance about date-passed status and unverified related series, which is useful, but it does not add per-parameter meaning; baseline 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 identifies the resource: institutional release schedules with primary source URLs, and the tool name 'get_release_calendar' reinforces the action. It distinguishes the tool from data-series siblings by focusing on schedules rather than indicator values, though it does not explicitly contrast it with any sibling.

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 usage context is implied by 'release schedules': an agent can infer this is for upcoming or past institutional release dates rather than historical data. However, the description offers no explicit when-to-use guidance, no exclusions, and does not name an alternative tool such as get_series_data or get_historical_data.

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?

No annotations are present, so the description carries the behavioral burden. It adds some useful context: the data is high-frequency and every response includes source licence and attribution. However, it does not explicitly describe the return structure, read-only nature, pagination, or any rate limits, though the license/attribution claim is a positive disclosure.

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 first sentence is long but purposeful, packing a comprehensive catalog of supported series into one readable statement. The second and third sentences are short and actionable. It could be structurally improved with bullets, but every sentence contributes useful information and the most important scoping detail is 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?

Given there is no output schema, the description should ideally spell out what the tool returns (time series observations, dates, values, units). It does not. It does provide rich context about data sources and series families, and the sibling list plus list_series pointer help with navigation, but return semantics and response shape are left to inference.

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 already has 100% description coverage for all parameters, so the baseline is 3. The description supplements the schema with many concrete series_id examples and clarifies the data families, but does not add new meaning about date formats, geo behavior, or limit semantics beyond what the schema already states.

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 makes clear this tool serves high-frequency series data across many named families (UST, SOFR, CPI, OECD, etc.), so an agent can infer it retrieves observations for a given series_id. However, it never explicitly states 'fetch observations for a series' and does not contrast with sibling tools like get_historical_data, leaving some ambiguity about exact scope.

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?

It gives one useful routing cue: 'Call list_series to browse.' This implies get_series_data is for known series IDs and list_series is for discovery. But it provides no guidance on when to prefer this over get_historical_data, compare_countries, or other siblings, and no when-not-to-use 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?

With no annotations available, the description carries the full burden for behavioral transparency. It discloses what the output contains (frequency, unit, source, licence for each series), which is helpful. However, it does not mention any such details as pagination, ordering, or potential limitations, though the simple nature of a list might not require them.

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 a single concise sentence, front-loaded with the verb and resource, and every phrase adds meaning (types of series and returned attributes). No wasted words.

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 that this is a parameterless list tool with no output schema and low complexity, the description adequately covers what the tool does and what it returns. It is complete for its intended purpose.

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 no parameters, so the description does not need to explain parameter semantics. The baseline for 0 params is 4, and the description provides no irrelevant parameter information.

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 action ('List') and the resource ('high-frequency series catalog'), and specifies the types of series (rates, yields, monthly labour and energy) and the attributes returned (frequency, unit, source, licence). This distinguishes it from sibling tools like get_series_data (which likely fetches data) and search_indicators (which searches for indicators).

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 browsing the catalog of high-frequency series when an overview of available series is needed, but it does not explicitly state when to use this tool instead of alternatives like get_available_indicators or search_indicators. No exclusions or alternatives are mentioned.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Search for indicators by keyword' without specifying the return format, search matching behavior (e.g., partial/fuzzy), or any other behavioral traits. This lack of detail could lead to incorrect assumptions about the tool's output.

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 concise sentences, starting with the action ('Search for indicators by keyword') and then providing the use case. There is no redundant wording or filler, making it easy to parse and front-loaded with essential 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?

For a low-complexity tool with one parameter, no annotations, and no output schema, the description adequately covers the purpose and usage. However, it omits the structure of the response (e.g., list of matching indicators with names and IDs). Since the description hints at finding indicator IDs, it is minimally sufficient but not fully complete.

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 fully documents the single 'query' parameter with examples ('gdp', 'population', etc.), achieving 100% schema coverage. The description does not add extra parameter semantics beyond what the schema already provides. Therefore, the baseline score 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 states a specific verb and resource: 'Search for indicators by keyword.' This clearly distinguishes it from sibling tools like get_available_indicators or list_series, which focus on listing all indicators or series. The phrase 'find the right indicator ID' reinforces the unique purpose of locating a specific indicator.

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 clear usage context: 'Use when you need to find the right indicator ID.' This tells the agent when to invoke the tool but does not explicitly mention alternatives or exclusions. Since sibling tools exist, a contrast would strengthen the guidance, but the context is sufficiently clear for a straightforward search scenario.

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