Meltema Weather

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 details the sampling behavior, cross-model spread aggregation, source defaults, variable alias normalization, and output carrying canonical IDs. This fully discloses the tool's behavior.

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 paragraph that communicates essential information efficiently. It is front-loaded with the primary action. However, it could be slightly more structured (e.g., bullet points for aliases) but remains concise and readable.

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, complete schema coverage, and existence of an output schema, the description covers all necessary aspects: purpose, inputs, defaults, alias mapping, and output behavior. It is fully adequate without missing details.

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%, but the description adds significant value beyond the schema: it explains how sources default to all live sources, lists canonical variable IDs and common aliases, and clarifies that the output uses canonical IDs. This meets the high bar when schema coverage is high.

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 core action: 'Sample one variable at a point across many models and summarize per-time cross-model spread (min/max/mean/spread).' It uses specific verbs and resources, and differentiates from siblings like point_forecast (single model) and describe_grid (grid description).

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 explains when to use the tool (to compare models at a point), the default source behavior, and how to pin sources. It also covers variable aliases. However, it does not explicitly state when NOT to use it or compare it to alternatives, though the sibling tool names imply the distinction.

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 of behavioral disclosure. It describes the output but does not disclose potential side effects, authentication requirements, or error behavior. The tool appears read-only, but no safety or destructive behavior is mentioned.

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 superfluous words. The first sentence fully explains the output, and the second provides a concise usage tip. 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?

An output schema exists, so the return format is documented elsewhere. The description explains the key contents of the output (geometry, URL, decode recipe). However, given the absence of annotations, a brief note on data volume or frequency of updates would improve 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?

Schema description coverage is 100% (all three parameters have descriptions). The description adds the same advice as the schema for init_time ('omit for latest run'), but does not add new semantic meaning beyond what the schema already provides. 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 states that the tool returns geometry (grid spec, leads, units), the exact API URL, and the decode recipe for raw bytes. This verb+resource combination distinguishes it from siblings like list_sources (which returns source IDs) and point_forecast (which returns point data).

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 includes a usage hint: 'Omit init_time for the latest run.' However, it does not explicitly state when to use this tool versus alternatives such as point_forecast or list_sources. The guideline is implied but lacks explicit when-not and alternative references.

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 fully disclose behavior. It mentions the ordering (newest first) and datetime format (UTC ISO 8601), but does not describe what happens with invalid source_id, absence of runs, pagination limits, or side effects. This leaves moderate 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 extremely concise with two sentences, front-loading the key output (list of init times) and then adding a practical usage note. Every sentence is meaningful with 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 the tool's low complexity (single parameter, full schema coverage, output schema present), the description covers the main purpose, output format, and ordering. The pinning mechanism is somewhat vague but likely clarified by output schema. Minor gaps exist around pagination and error cases, but overall sufficient.

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% with a clear description for source_id. The description does not add further meaning beyond the schema's example ('e.g., gfs') and the high-level use of the return values. No additional parameter semantics are provided.

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 stored run init times for a source, sorted newest first in UTC ISO 8601 format. It also mentions the secondary purpose of pinning a tool to a run. The verb 'list' and resource 'stored runs' are unambiguous, and the tool is distinct from siblings like list_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 implies usage by stating the output format and the pinning action, but it does not explicitly say when to use this tool versus alternatives like compare_models or describe_grid. There are no exclusions or alternative tool names 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, the description carries the full burden. It discloses that the output includes live-data status, servable variables, cadence, and licensing attribution. It doesn't describe side effects (none expected) or pagination (no parameters). The description is fairly transparent.

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, front-loaded with purpose, no extraneous information. Every sentence 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 parameters and an output schema, the description covers key attributes and explicitly links to sibling tools. It is complete for a list 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?

No parameters; baseline is 4. The description adds value by explaining what the output contains, which is useful 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 clearly states the tool lists public forecast sources with specific attributes (live-data status, servable variables, cadence, licensing). It distinguishes from siblings by noting it is the live enum of source_id used by other tools.

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 states this provides the live enum of source_id accepted by other tools, implying it should be used to get valid source IDs before using sibling tools. It does not include when not to use, but the context 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?

With no annotations provided, the description carries full burden. It explains default selections for source, init_time, and variables, and details variable aliases. However, it does not disclose potential side effects, authentication requirements, rate limits, or error conditions.

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 paragraph that efficiently conveys all necessary information without redundancy. It front-loads the core purpose and provides detailed parameter guidance in a compact format.

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 (5 parameters, 2 required, no enums, output schema present), the description covers defaults, variable mapping, and source selection. It is complete enough for typical use, though it could mention error handling or response structure (mitigated by output schema).

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%, and the description adds significant value beyond schema by explaining default behavior for source_id, init_time, and variables, listing canonical variable IDs and their aliases, and clarifying that the output uses canonical IDs.

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 'Full forecast horizon of one model at a single point (bilinear grid sample)', which is a specific verb-resource pair. It distinguishes itself from siblings like compare_models or list_sources by focusing on point-level forecast data.

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 explicit guidance on optional parameters: source_id, init_time, and variables. It explains when to omit them and what defaults apply. However, it does not explicitly state when to use alternatives like compare_models for multi-model comparison.

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