World Bank & FRED Macro

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 discloses the default behavior for the year parameter (most recent available), but omits key behavioral traits: whether the operation is read-only, what happens if data is missing for some countries, rate limits, or how the 'most recent' year is determined. This leaves the agent with significant uncertainty.

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 with no wasted words, front-loading the core purpose. However, its brevity sacrifices necessary detail for other dimensions, making it efficient but not optimally helpful. A bit more structure (e.g., bullet points on parameters) could improve clarity without increasing length significantly.

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 (3 parameters, no output schema, no annotations, 0% schema coverage), the description is incomplete. It does not explain the return format, error handling, pagination (if any), or how to specify year format. Without an output schema, the description should outline what the agent can expect in the response.

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 0% schema description coverage, the description must compensate for all three parameters. It only addresses the 'year' parameter with a default note, but 'countries' and 'indicator' are left unexplained beyond their names. The description adds minimal meaning, failing to clarify what constitutes a valid indicator identifier or country code.

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: side-by-side comparison of an indicator across multiple countries, with an optional year defaulting to the most recent. This specific verb+resource structure distinguishes it from sibling tools like fred_search_series or wb_search_indicator, which focus on searching rather than comparing.

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 guidance on when to use this tool versus alternatives. It only implies usage for comparing indicators, but fails to mention prerequisites, scenarios where other tools are better, or any when-not-to-use advice. The sibling tools suggest a family of indicator-related tools, yet no comparative usage is described.

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 should disclose behavioral traits. It only states it is a free-text search and returns fields, but no details on pagination, rate limits, result limits, or error handling.

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 (one sentence) and starts with action. However, it could incorporate parameter guidance without significant length increase.

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 tool with one parameter and no output schema, the description covers basic purpose and return fields but lacks completeness on usage context, such as handling of empty results or search modes.

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 single parameter 'query' is not described at all in the description despite 0% schema coverage. The description does not add any meaning beyond the schema, such as format, encoding, or examples.

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 performs free-text search across FRED series and lists return fields (id, title, units, frequency). However, it does not differentiate from sibling tool 'fred_series', which may be for exact series lookup.

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 guidance on when to use this tool versus alternatives like 'fred_series' or other search tools. Missing context for optimal usage.

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, and the description does not disclose behaviors such as output format, rate limits, or whether data is returned as a time series. It only states 'observations' without elaboration.

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 exceptionally concise—just one sentence that conveys the core purpose and provides illustrative examples. 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 no output schema and the tool's data retrieval nature, the description lacks crucial context about the return format (e.g., time series vs single value), date range behavior, or default parameters. It feels incomplete for effective use.

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 adds value for series_id by listing examples, but date_from and date_to have no description beyond the schema's minimal notation for date_from. With schema coverage at 33%, the description partially compensates but not fully.

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 indicates the tool retrieves FRED series observations and provides examples of well-known series IDs, making the purpose evident. However, it lacks an explicit verb like 'retrieve' or 'get', slightly reducing specificity.

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 guidance is given on when to use this tool versus siblings like fred_search_series. It does not specify prerequisites or typical use cases, leaving the agent to infer context.

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 must convey behavioral traits. It states it returns a 'time series' but does not specify default date ranges, output structure, error handling, or side effects. Given the lack of annotations, more detail is needed for transparency.

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 purpose, and contains no unnecessary words. Every sentence adds value: purpose, example IDs, and alternative tool.

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 4 parameters, no output schema, and no annotations, the description is too sparse. It lacks details about return format, default behavior for year parameters, error cases, and limitations. More context is needed for a complex 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 50% (country and indicator have descriptions). The description adds value by listing common indicator IDs, but does not address year_from or year_to parameters at all. It partially compensates for the low coverage but not fully.

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 a World Bank time series for a single indicator and country, specifying verb ('time series') and resource. It distinguishes from siblings by emphasizing 'single indicator and country' and explicitly directing users to wb_search_indicator for unknown IDs.

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 common indicator IDs and explicitly names the alternative tool wb_search_indicator for unknown IDs. It does not explicitly state when not to use this tool, but the context and alternative make usage 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 provided, so the description carries the full burden. It mentions 'free-text search' and a limit of 25 matches, which is helpful, but it lacks details on behavior like pagination, error handling, or matching logic. More disclosure is needed for a search tool.

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 extremely concise, consisting of two short sentences that front-load the key information: the action and the output. Every word adds value, making it efficient for an AI agent to parse quickly.

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 parameter, no output schema, no annotations), the description adequately covers the basic purpose and output format. However, it lacks details on error cases, return structure, or edge cases, which would be expected for full 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 input schema has 0% description coverage and one parameter 'query'. The description adds basic meaning by calling it a 'free-text search', but does not specify allowed formats, length limits, or case sensitivity. With zero schema coverage, more compensation is expected.

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 verb 'search', the resource 'World Bank indicator names', and the output 'matching indicator IDs + names'. It is specific enough to understand the tool's function, but does not explicitly differentiate from sibling tools like wb_indicator or fred_search_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?

There is no guidance on when to use this tool versus alternatives such as compare_countries or wb_indicator. The usage context is implied but not explicitly stated, and no exclusions or prerequisites are provided.

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