Server Quality Checklist

Tool Scores

• air_qualityC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full burden. It mentions 'forecast data' but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or whether this is a read-only operation. The description is minimal and lacks context about what the tool actually does beyond the basic purpose.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is a single, efficient sentence that states the core purpose. It's appropriately sized for a simple data retrieval tool, though it could be slightly more informative without losing conciseness. No wasted words or redundant information.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given no annotations and no output schema, the description is incomplete. It doesn't explain what format the data returns, whether it's real-time or historical, or any limitations. For a tool with 6 parameters and environmental data complexity, more context about behavior and output would be helpful for an AI agent.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no parameter-specific information beyond mentioning some pollutants that map to the 'hourly' enum values. It doesn't explain parameter interactions or provide additional context, meeting the baseline for high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get air quality forecast data including PM2.5, PM10, ozone, nitrogen dioxide and other pollutants.' It specifies the verb ('Get') and resource ('air quality forecast data') with examples of pollutants. However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'climate_projection' which might overlap in environmental data domains.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or clarify specific use cases (e.g., air quality vs. general weather forecasting). The agent must infer usage from the name and description alone, with no explicit when/when-not instructions.

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

• climate_projectionC
  2.9/5.0
  Behavior2/5

  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. While 'Get' implies a read-only operation, the description doesn't address critical behavioral aspects: whether this requires authentication, rate limits, data freshness, computational cost, error handling, or what format the projections return. For a complex tool with 10 parameters and no output schema, this is a significant gap in transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every word earns its place: 'Get' (action), 'climate change projections' (resource), 'from CMIP6 models' (source), 'for different warming scenarios' (context). No redundancy or fluff.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., time series data, statistical summaries), how projections are generated, or prerequisites like data access. For a climate projection tool with significant parameter requirements, more context is needed to guide effective use.

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

  Parameters3/5

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

  Schema description coverage is 70%, providing a solid baseline. The description adds minimal parameter semantics beyond the schema—it mentions 'warming scenarios' which might relate to models or parameters, but doesn't clarify how parameters like 'daily' array choices or model selections affect the projections. The description doesn't compensate for the 30% coverage gap in undocumented parameters.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get climate change projections from CMIP6 models for different warming scenarios.' It specifies the action ('Get'), resource ('climate change projections'), and source ('CMIP6 models'), which is more specific than just restating the name. However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'seasonal_forecast' that might also provide climate-related data.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like 'weather_forecast', 'seasonal_forecast', and 'ensemble_forecast' that might overlap in functionality, there's no indication of this tool's specific niche (e.g., long-term climate projections vs. short-term weather forecasts). Usage context is implied but not stated.

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

• dwd_icon_forecastC
  2.9/5.0
  Behavior2/5

  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. While it mentions the data source and coverage, it doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, accuracy characteristics, or what format the forecast data returns. For a complex 11-parameter tool with no annotations, this is a significant gap.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that packs substantial information: action, source, data quality, and coverage. Every word earns its place with zero wasted verbiage. It's appropriately sized and front-loaded with the core functionality.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a complex weather forecasting tool with 11 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how the forecast data is structured, what time ranges are available, or any limitations of the DWD ICON model. The agent would struggle to understand how to effectively use this tool beyond the basic parameter documentation.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'high resolution data' which relates to the forecast quality but doesn't provide additional context about parameter usage, relationships between parameters, or best practices for selecting hourly/daily variables.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the action ('Get weather forecast') and specifies the source ('German DWD ICON model'), scope ('Europe and global coverage'), and data quality ('high resolution data'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'ecmwf_forecast' that might provide similar weather data from different sources.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With multiple weather-related sibling tools (ecmwf_forecast, gfs_forecast, weather_forecast, etc.), there's no indication of what makes the DWD ICON model preferable or when it should be chosen over other forecast models.

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

• ecmwf_forecastC
  2.9/5.0
  Behavior2/5

  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. While it mentions 'high-quality global forecasts,' it doesn't describe critical behavioral aspects: whether this is a read-only operation, rate limits, authentication requirements, data freshness, error conditions, or what the output format looks like. For a complex 11-parameter tool with no annotations, this is a significant gap in behavioral transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that gets straight to the point: 'Get weather forecast from European Centre for Medium-Range Weather Forecasts with high-quality global forecasts.' Every word earns its place - it specifies the action, source, and quality without any fluff or redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (11 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain what the tool returns, how forecasts are structured, time ranges covered, data resolution, or any limitations. For a weather forecast tool with numerous configuration options and no output schema, users need more context about what to expect from the response.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly with descriptions, ranges, enums, and defaults. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from European Centre for Medium-Range Weather Forecasts with high-quality global forecasts.' It specifies the action ('Get weather forecast'), the source (ECMWF), and a quality characteristic. However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'gfs_forecast' that might offer similar functionality from different sources.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools offering weather forecasts (e.g., 'weather_forecast', 'gfs_forecast', 'metno_forecast'), there's no indication of what makes ECMWF forecasts unique or when they should be preferred. The description mentions 'high-quality global forecasts' but doesn't explain how this compares to other forecast sources.

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

• elevationC
  2.9/5.0
  Behavior2/5

  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 mentions the data source ('digital elevation models') but doesn't disclose critical traits like accuracy, resolution, rate limits, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond the basic operation.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('Get elevation data', 'for given coordinates', 'using digital elevation models') contributes essential information, making it appropriately sized and well-structured.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's moderate complexity (geospatial data retrieval) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., elevation in meters), potential errors, or limitations (e.g., coverage areas). For a tool with no structured output information, more context is needed to ensure proper usage.

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

  Parameters3/5

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

  Schema description coverage is 100%, with clear parameter descriptions in the schema (e.g., 'Latitude in WGS84 coordinate system'). The description adds no additional parameter semantics beyond what the schema provides, such as coordinate precision or expected units. Baseline 3 is appropriate since the schema adequately documents the parameters.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose with a specific verb ('Get') and resource ('elevation data'), and specifies the data source ('using digital elevation models'). It distinguishes from siblings by focusing on elevation rather than weather, climate, or other geospatial data. However, it doesn't explicitly differentiate from potential overlapping tools like geocoding that might also handle coordinates.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'geocoding' (which might convert addresses to coordinates) or other environmental tools, nor does it specify prerequisites or exclusions (e.g., coordinate validity, data availability). Usage is implied only through the purpose statement.

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

• ensemble_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions 'forecast uncertainty' but doesn't explain how this is represented (e.g., probability distributions, confidence intervals). It also omits critical details like rate limits, authentication requirements, data freshness, or response format. For a complex forecasting tool with 10 parameters, 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.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose. It wastes no words and directly communicates the tool's function. Every part of the sentence ('Get ensemble forecasts', 'showing forecast uncertainty', 'with multiple model runs') contributes essential information.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a complex forecasting tool with 10 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output contains (e.g., uncertainty metrics, time series), how ensemble results are aggregated, or any behavioral constraints. The agent lacks sufficient context to understand what the tool actually returns or its operational characteristics.

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

  Parameters3/5

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

  The description doesn't add any parameter-specific information beyond what's in the schema. With 70% schema description coverage, the schema already documents most parameters well (e.g., latitude/longitude descriptions, enum lists for models/hourly/daily). The description's mention of 'multiple model runs' aligns with the 'models' parameter but doesn't provide additional context. Baseline 3 is appropriate given the schema does most of the work.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get ensemble forecasts showing forecast uncertainty with multiple model runs.' It specifies the verb ('Get'), resource ('ensemble forecasts'), and key feature ('showing forecast uncertainty with multiple model runs'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'ecmwf_forecast', which likely provide single-model forecasts.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or specify scenarios where ensemble forecasts are preferable (e.g., for uncertainty quantification vs. deterministic forecasts). Without such context, the agent must infer usage from the tool name and description alone.

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

• flood_forecastC
  2.9/5.0
  Behavior2/5

  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 states the tool 'Get[s]' data, implying a read-only operation, but doesn't address key behavioral aspects such as rate limits, authentication requirements, data freshness, error handling, or response format. For a tool with 7 parameters and no output schema, this is a significant gap in transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every part of the sentence contributes essential information, earning its place.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations, which are crucial for effective tool invocation. While the schema covers parameters well, the description doesn't compensate for missing annotations or output schema, leaving gaps in overall understanding.

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

  Parameters3/5

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

  The description adds no specific parameter information beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents all parameters thoroughly (e.g., latitude/longitude ranges, daily enum values, past_days/forecast_days limits). The baseline score of 3 is appropriate as the schema does the heavy lifting, but the description doesn't enhance parameter understanding with additional context or examples.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get river discharge and flood forecasts from GloFAS (Global Flood Awareness System).' It specifies the action ('Get'), resource ('river discharge and flood forecasts'), and data source ('GloFAS'), which is specific and informative. However, it doesn't explicitly differentiate from siblings like 'weather_forecast' or 'ensemble_forecast', which might also provide forecast data, though the focus on river/flood data is implied.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'weather_forecast' or 'climate_projection', leaving the agent to infer usage based on the purpose alone. This lack of explicit context reduces its effectiveness in tool selection.

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

• gem_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions the data source ('Canadian weather service GEM model') and geographic coverage ('Canada and North America'), but doesn't address critical behavioral aspects: whether this is a read-only operation, rate limits, authentication requirements, data freshness, or what the response format looks like. For a weather API tool with 11 parameters, this leaves significant gaps in understanding how the tool behaves.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is a single, efficient sentence that communicates the core purpose. It's appropriately sized for a weather forecast tool, though it could potentially be more front-loaded by mentioning the geographic scope earlier. There's no wasted language, and every word contributes to understanding what the tool does.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (11 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain what the tool returns, how forecasts are structured, time ranges covered, or data resolution. For a weather forecasting tool competing with 15 siblings, users need more context about output format, data quality, and use cases to select this tool appropriately.

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

  Parameters3/5

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

  The description provides no parameter-specific information beyond what's already in the schema. However, with 100% schema description coverage and detailed parameter documentation (including enums, defaults, and constraints), the schema does the heavy lifting. The baseline score of 3 is appropriate since the schema fully documents all 11 parameters, though the description adds no additional parameter context.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from Canadian weather service GEM model with high-resolution data for Canada and North America.' It specifies the verb ('Get'), resource ('weather forecast'), and geographic scope ('Canada and North America'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'gfs_forecast', which likely provide similar weather data from different sources.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With 15 sibling tools including 'weather_forecast', 'gfs_forecast', and 'ecmwf_forecast', there's no indication of when GEM model forecasts are preferable (e.g., for Canadian regions, high-resolution needs, or specific weather variables). The description mentions 'high-resolution data for Canada and North America' but doesn't make this a clear usage recommendation.

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

• gfs_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions the data source (NOAA GFS model) and coverage but does not disclose critical traits such as rate limits, authentication needs, data freshness, error handling, or whether it's a read-only operation. For a tool with 11 parameters and no annotations, this is a significant gap in transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does and adds relevant context (model and coverage), making it highly concise and well-structured for quick understanding.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity with 11 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, usage guidelines, and output format, which are crucial for an AI agent to invoke the tool correctly. The high parameter count and absence of structured support make the description insufficient for full contextual understanding.

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

  Parameters3/5

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

  Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no specific parameter details beyond implying forecast retrieval, so it does not compensate for any gaps. However, since the schema is comprehensive, the baseline score of 3 is appropriate as the description does not detract but adds minimal value.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from US NOAA GFS model' specifies the verb ('Get') and resource ('weather forecast'), and it adds context about global coverage and high-resolution data for North America. However, it does not explicitly differentiate from sibling tools like 'weather_forecast' or 'dwd_icon_forecast', which might offer similar services, so it lacks sibling distinction for a top score.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It mentions the model (GFS) and coverage details but does not specify scenarios, prerequisites, or exclusions compared to siblings like 'ecmwf_forecast' or 'ensemble_forecast'. This absence of usage context leaves the agent without clear direction for tool selection.

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

• jma_forecastC
  2.9/5.0
  Behavior2/5

  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 states the tool retrieves 'high-resolution data,' which adds some context about data quality, but lacks critical details such as rate limits, authentication requirements, data freshness, error handling, or response format. For a tool with 11 parameters and no output schema, this is a significant gap in transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does, the source, and the scope, making it easy to understand at a glance. Every part of the sentence earns its place by providing essential information.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (11 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects like rate limits or errors, doesn't explain the output structure, and provides no usage guidelines. While the schema covers parameters well, the overall context for effective tool use is insufficient, especially for a data retrieval tool with many configuration options.

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

  Parameters3/5

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

  Schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description doesn't add any additional meaning or context about the parameters beyond what's already in the schema (e.g., it doesn't explain how 'high-resolution' relates to parameters like 'forecast_days' or 'hourly'). Baseline score of 3 is appropriate as the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from Japan Meteorological Agency with high-resolution data for Japan and Asia.' It specifies the action ('Get weather forecast'), source ('Japan Meteorological Agency'), and scope ('Japan and Asia'), but doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'dwd_icon_forecast' that might provide similar functionality for other regions or sources.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  No guidance is provided on when to use this tool versus alternatives. The description mentions 'Japan and Asia' as the scope, but it doesn't clarify if this is the only tool for that region or when to choose it over other forecast tools like 'gfs_forecast' or 'ecmwf_forecast' that might also cover Asia. There's no mention of prerequisites, limitations, or specific use cases.

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

• meteofrance_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions the source models (AROME, ARPEGE) and their geographic focus, which adds some context. However, it doesn't describe critical behaviors like rate limits, authentication needs, data freshness, error handling, or response format. For a tool with 11 parameters and no output schema, this is a significant gap in transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose. It wastes no words and directly states what the tool does, including key details about models. Every part of the sentence earns its place by adding value (e.g., specifying the French source and model names).

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (11 parameters, no annotations, no output schema), the description is incomplete. It lacks behavioral details (e.g., rate limits, auth), usage guidance versus siblings, and any explanation of return values or errors. While concise, it doesn't provide enough context for an agent to confidently use this tool without relying heavily on the schema alone.

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

  Parameters3/5

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

  Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no specific parameter information beyond what's in the schema (e.g., it doesn't explain how 'hourly' or 'daily' arrays interact, or clarify model-specific constraints). With high schema coverage, the baseline is 3, as the description doesn't compensate with additional param semantics.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from French Météo-France models including AROME (high-resolution France) and ARPEGE (Europe).' It specifies the action ('Get weather forecast'), the source ('French Météo-France models'), and mentions specific models (AROME, ARPEGE). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'ecmwf_forecast', which likely provide similar weather data from different sources.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. It mentions the models (AROME for France, ARPEGE for Europe), which implies geographic scope, but doesn't explicitly state when to choose this over sibling tools like 'weather_forecast' or 'ecmwf_forecast'. No exclusions, prerequisites, or comparative advice are given.

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

• metno_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions 'high-resolution data for Nordic countries,' which adds some context about data quality and regional focus, but fails to address critical aspects like rate limits, authentication needs, error handling, or what the response format looks like. For a tool with 11 parameters and no output schema, this is a significant gap.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence earns its place by specifying the action, source, data quality, and geographic scope.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (11 parameters, no annotations, no output schema, and multiple sibling alternatives), the description is incomplete. It lacks usage guidelines, behavioral details (e.g., rate limits, response format), and differentiation from other weather tools. The high parameter count and absence of output schema mean the description should do more to guide effective use.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain the meaning of 'high-resolution' in relation to parameters like 'hourly' or 'forecast_days'). Baseline 3 is appropriate when the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast from Norwegian weather service with high-resolution data for Nordic countries.' It specifies the action ('Get weather forecast'), the data source ('Norwegian weather service'), and the geographic focus ('Nordic countries'). However, it doesn't explicitly differentiate from sibling tools like 'weather_forecast' or 'meteofrance_forecast' beyond mentioning the Norwegian source.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With multiple weather-related sibling tools (e.g., 'weather_forecast', 'dwd_icon_forecast', 'meteofrance_forecast'), there's no indication of when this Norwegian service is preferred, what makes it unique, or any prerequisites for use.

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

• seasonal_forecastC
  2.9/5.0
  Behavior2/5

  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 mentions 'long-range seasonal forecasts' but doesn't specify data sources, accuracy, update frequency, rate limits, authentication needs, or what the output looks like (since there's no output schema). For a complex forecasting tool with 12 parameters, this is a significant gap in 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.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does ('Get long-range seasonal forecasts') and key constraints ('for temperature and precipitation up to 9 months ahead'), making it easy to parse quickly. Every word earns its place.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (12 parameters, no annotations, no output schema, and many sibling tools), the description is incomplete. It lacks crucial context: no output details, no behavioral traits (e.g., data latency, reliability), no differentiation from siblings, and minimal parameter guidance. For a forecasting tool with significant parameter interplay, this leaves the agent under-informed.

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

  Parameters3/5

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

  Schema description coverage is 75%, which is relatively high, so the baseline is 3. The description adds minimal parameter semantics beyond the schema—it implies parameters for location and time range but doesn't explain relationships (e.g., how 'forecast_days' interacts with 'start_date'/'end_date') or provide usage examples. It doesn't compensate for the 25% coverage gap (e.g., units like 'temperature_unit' are only in the schema).

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get long-range seasonal forecasts for temperature and precipitation up to 9 months ahead.' It specifies the action ('Get'), resource ('seasonal forecasts'), and scope ('temperature and precipitation up to 9 months ahead'). However, it doesn't explicitly differentiate from siblings like 'climate_projection' or 'ensemble_forecast' that might also provide forecast data, which prevents a perfect score.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'weather_forecast', 'ensemble_forecast', 'climate_projection'), there's no indication of what makes 'seasonal_forecast' unique or when it's preferred over others. This lack of context leaves the agent guessing about appropriate usage scenarios.

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

• weather_archiveC
  2.9/5.0
  Behavior2/5

  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 states the data source (ERA5 reanalysis) and temporal range (1940-present), but lacks critical details such as rate limits, authentication needs, data format, error handling, or whether it's a read-only operation. For a tool with 8 parameters and no output schema, this is a significant gap.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('Get historical weather data,' 'ERA5 reanalysis,' '1940-present,' 'specific coordinates and date range') contributes directly to understanding the tool's function.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like data retrieval limits, response format, or error conditions, nor does it provide usage guidelines relative to siblings. For a historical data tool with rich parameters, more context is needed to ensure effective agent use.

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

  Parameters3/5

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

  The description adds minimal parameter semantics beyond the schema, which has high coverage (88%). It mentions 'coordinates and date range,' aligning with the required parameters (latitude, longitude, start_date, end_date), but doesn't explain the hourly/daily arrays or optional parameters like temperature_unit. Baseline 3 is appropriate given the schema's thorough documentation.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the action ('Get historical weather data') and resource ('from ERA5 reanalysis'), with specific scope ('1940-present') and constraints ('for specific coordinates and date range'). It distinguishes from forecast-oriented siblings like 'weather_forecast' by emphasizing historical data, though it doesn't explicitly name alternatives.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  No explicit guidance on when to use this tool versus alternatives is provided. While the description implies historical data retrieval, it doesn't mention when to choose this over other weather tools (e.g., 'climate_projection' for future scenarios or 'weather_forecast' for predictions), nor does it discuss prerequisites or exclusions.

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

• weather_forecastC
  2.9/5.0
  Behavior2/5

  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. While it mentions the API source (Open-Meteo) and data types (hourly/daily), it lacks critical behavioral information: whether this is a read-only operation, what authentication might be required, rate limits, error conditions, response format, or whether it makes external API calls. For a tool with 11 parameters and no output schema, this leaves significant gaps in understanding how the tool behaves.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the primary purpose, and the second adds key capability information. There's no wasted language or redundancy. However, it could be slightly more front-loaded with distinguishing information given the many sibling tools.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity (11 parameters, no annotations, no output schema, 16 sibling tools), the description is insufficiently complete. It doesn't explain what the tool returns (no output schema exists), doesn't help distinguish it from alternatives, and provides minimal behavioral context. For a data retrieval tool with many configuration options and similar alternatives, the description should do more to guide proper use and set expectations.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly with descriptions, enums, defaults, and constraints. The description adds minimal value beyond the schema - it mentions 'hourly and daily data with various weather variables' which corresponds to two parameters, but doesn't provide additional context about parameter interactions, dependencies, or usage patterns. This meets the baseline expectation when schema coverage is complete.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get weather forecast data for coordinates using Open-Meteo API.' It specifies the verb ('Get'), resource ('weather forecast data'), and scope ('for coordinates'), which is specific and actionable. However, it doesn't explicitly differentiate this tool from its many siblings (like 'gfs_forecast', 'ecmwf_forecast', etc.), which all appear to be weather-related tools on the same server.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With 16 sibling tools on the server (including 'gfs_forecast', 'ecmwf_forecast', 'air_quality', etc.), the agent receives no help in selecting this specific Open-Meteo-based forecast tool over other weather data sources. The mention of 'Supports hourly and daily data with various weather variables' describes capability but not selection criteria.

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

• marine_weatherB
  3.1/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool retrieves forecast data but doesn't describe critical behaviors: whether it's a read-only operation, requires authentication, has rate limits, returns real-time or historical data, or how forecasts are generated. The mention of 'forecast' implies future data, but behavioral context is minimal.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

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

  The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('Get marine weather forecast including...') directly contributes to understanding the tool's function. There's zero waste or redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's moderate complexity (7 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context about behavioral traits, usage compared to siblings, or output format. Without annotations or output schema, the description should ideally provide more operational context, but it meets a basic threshold for a data retrieval tool.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds no parameter-specific semantics beyond implying location-based queries through 'marine weather forecast'. It lists example data points (wave height, period, etc.) that correspond to 'hourly' enum values, but this doesn't provide additional meaning beyond what the schema already specifies with its enum arrays and descriptions.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get marine weather forecast including wave height, wave period, wave direction and sea surface temperature.' It specifies the verb ('Get') and resource ('marine weather forecast') with concrete examples of data points. However, it doesn't explicitly differentiate from sibling weather tools like 'weather_forecast' or 'dwd_icon_forecast' beyond mentioning 'marine' focus.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools for weather, climate, and forecasts (e.g., 'weather_forecast', 'ensemble_forecast', 'climate_projection'), there's no indication of when this marine-specific tool is appropriate or what distinguishes it from general weather tools. Usage is implied by the 'marine' keyword but not explicitly stated.

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

• geocodingA
  4.1/5.0
  Behavior3/5

  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 explains what the tool returns ('geographic coordinates and detailed location information') and its essential purpose in converting names to coordinates. However, it doesn't mention rate limits, authentication requirements, error conditions, or whether this is a read-only operation versus a write operation. The description adds value but leaves significant behavioral aspects unspecified.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

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

  The description is appropriately sized and front-loaded, with the core purpose stated in the first sentence. The second and third sentences provide valuable usage context. While efficient, the third sentence could be slightly more concise by combining concepts about when to use the tool.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a search tool with no annotations and no output schema, the description provides good context about what the tool does and when to use it. It clearly distinguishes this from sibling weather tools. However, without annotations or output schema, it could better explain the return format, error handling, or limitations of the geocoding service.

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

  Parameters3/5

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

  Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions the 'name' parameter implicitly ('location name or postal code') but provides no additional syntax, format, or usage details. Baseline 3 is appropriate when the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose with specific verbs ('search for locations', 'convert a location name into precise coordinates') and resources ('geographic coordinates', 'detailed location information'). It explicitly distinguishes this geocoding tool from sibling weather/forecast tools by focusing on coordinate conversion rather than meteorological data.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit guidance on when to use this tool: 'when you need to convert a location name into precise coordinates that are required by other tools' and 'when you have a location name but need coordinates for data fetching tools.' This clearly differentiates it from sibling tools that provide weather data rather than coordinate conversion.

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