ILOSTAT — ILO Labour Statistics (provenance-first)

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

Annotations already mark the tool as read-only and idempotent, and the description adds non-obvious behaviors: max area limit, pagination advice, unfiltered dimensions return all categories, and raw data is returned without transformation. This goes beyond the safety profile provided by 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 compact at two sentences, packing crucial details (purpose, filter example, limits, alternatives) without redundancies. It is front-loaded with the core function.

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 rich input schema, annotations, and an output schema, the description covers the essential context: purpose, usage constraints, and exclusions. It is detailed enough for an agent to select and invoke the tool correctly without 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?

The schema already describes all parameters (100% coverage), but the description adds practical semantics like the required REF_AREA and its 30-area limit, along with example filters and period formats. It also clarifies the difference between period-based and last_n_observations selection.

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 returns statistical observations from one ILOSTAT dataflow, which is a specific verb+resource. It distinguishes from siblings by explicitly mentioning it does not search indicators and directs users to ilo_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?

It provides explicit usage guidance: REF_AREA is required with a maximum of 30 areas, and advises splitting broad panels into batches or paginating by period. It also states an alternative tool for searching indicators, making when-to-use 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?

Annotations mark it read-only, idempotent, and non-destructive. The description adds behavioral context beyond annotations by stating it does not return statistical values and does not list codes, which clarifies its scope and complementary role relative to siblings.

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, front-loaded with the core function, and every phrase adds value. No redundancy or excess length.

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 output schema exists and annotations are enriched, the description adds all necessary context: placement in workflow, limitations, and alternatives. It is complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description itself does not add parameter-specific semantics beyond what the schema provides, 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 explains the tool provides the structure of an ILOSTAT dataflow, including dimensions, codelists, time dimension, default selection, and data vintage. It distinguishes from sibling tools by explicitly contrasting with ilo_get_data and ilo_list_dimension_values.

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?

Explicitly states when to use it: 'Use before ilo_get_data to know which filters exist.' It also specifies what it does NOT do and directs users to an alternative for code values, giving clear usage 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 declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond annotations: it returns only codes (not statistical values) and explicitly excludes the time dimension, which are important behavioral constraints.

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, front-loading the core purpose, and every clause adds value—examples, usage guidance, and exclusions. There is no redundancy or filler.

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 simple lookup nature, the presence of an output schema, and thorough annotations, the description effectively covers purpose, usage, and key limitations. It tells users exactly what they need to know to call the tool correctly without over-explaining.

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%, so the baseline is 3. The description adds examples of dimension ids (REF_AREA, SEX) but doesn't provide additional parameter-specific meaning beyond the schema. It neither harms nor significantly enhances parameter understanding.

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?

Description clearly states it returns valid codes (id + label) for a dimension of an ILOSTAT dataflow, with concrete examples (REF_AREA, SEX). It explicitly differentiates from siblings by noting it does not return statistical values, making its purpose unambiguous.

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 explicitly says to use it to build correct ilo_get_data filters, and provides a clear exclusion: it is not applicable to the time dimension, directing time filtering to start_period/end_period in ilo_get_data. This gives both when-to-use and 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 convey read-only, idempotent, non-destructive behavior. The description adds contextual behavior: it searches catalogue metadata by keywords, returns dataflow IDs, and does not return statistical data. This supplements the annotation without contradicting 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 three sentences, front-loaded with the primary function, followed by an example and explicit exclusions. Every sentence contributes distinct information with no redundancy or filler, achieving excellent conciseness.

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, the description need not detail return structure. It covers what it does, what it returns, what it doesn't do, and provides a usage example, making it fully self-contained for the agent to correctly select and invoke the 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?

Schema description coverage is 100%, so the baseline is 3. The description adds a concrete keyword example ("unemployment rate sex age") and clarifies the output's purpose (dataflow ids for downstream tools), which enriches understanding of the query parameter beyond the schema alone.

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 function: "Search the ILOSTAT catalogue of ~1,200 indicator dataflows by keywords in the name or id" with a concrete example. It distinguishes itself from siblings by explicitly noting it does not return statistical values (use ilo_get_data) and does not cover non-ILO sources, making its purpose unambiguous.

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 explicit guidance: the tool returns dataflow ids for use with ilo_get_data / ilo_get_indicator_metadata, and explicitly excludes statistical values and non-ILO sources. This tells the agent when to choose this tool and when to pick an alternative.

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