Server Quality Checklist

Tool Scores

• get_risk_assessment_scorecardA
  3.8/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses that the tool retrieves data (implying read-only) and mentions output formats (Markdown/JSON), but lacks details on permissions, rate limits, error handling, or what the scorecard contains. It adds some context (e.g., format options) but doesn't fully compensate for the missing annotations.

  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 well-structured with clear sections ('When to use this tool' and 'How to use this tool'), and sentences are purposeful. However, it includes implementation advice ('use an MDX-style React component') that may be unnecessary for tool selection, slightly reducing efficiency.

  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 no annotations and no output schema, the description is moderately complete. It covers purpose, usage, and basic parameters, but lacks details on behavioral traits (e.g., auth, errors) and output structure. For a tool with 2 parameters and no structured output info, it should provide more context on what the scorecard contains or how to interpret it.

  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 both parameters ('session_id' and 'as_json'). The description adds minimal value beyond the schema: it mentions 'Markdown format' and implies JSON as an alternative, but doesn't explain parameter interactions or provide additional semantics. Baseline 3 is appropriate when 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: 'retrieves the risk assessment scorecard for the Chronulus Session in Markdown format'. It specifies the verb ('retrieves'), resource ('risk assessment scorecard'), and format ('Markdown format'), though it doesn't explicitly distinguish it from sibling tools like 'save_forecast' or 'save_prediction_analysis_html', which are write operations.

  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 the user asks about the risk level or safety concerns of a forecasting use case' and 'to provide justification... if you would like to warn them'. It clearly defines the context for usage, though it doesn't mention alternatives or exclusions, which is acceptable given the specificity of the use cases.

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

• save_prediction_analysis_htmlA
  3.8/5.0
  Behavior3/5

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

  With no annotations provided, the description carries full burden. It discloses that the tool saves files to disk (implied by output_path/html_name), provides status updates through MCP context, and requires specific inputs. However, it doesn't mention error conditions, file overwrite behavior, permission requirements, or what happens if the request_id is invalid. For a file-writing tool with no annotations, this leaves important behavioral gaps.

  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 well-structured with clear sections (purpose, when to use, how to use). It's appropriately sized at 4 paragraphs. While efficient, the 'How to use' section could be more concise by combining related points, but overall it's well-organized and front-loaded with key information.

  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 8 required parameters, no annotations, and no output schema, the description provides good usage guidance but lacks important context. It doesn't explain what the HTML output looks like beyond mentioning plots and expert opinions, doesn't cover error handling, and doesn't specify file format requirements beyond '.html' extension. For a complex tool with many parameters, this leaves significant gaps.

  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 8 parameters thoroughly. The description mentions request_id, output_path, and html_name in the 'How to use' section, but doesn't add meaningful semantic context beyond what's in the schema. The baseline of 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: 'saves an analysis of a BinaryPredictor prediction to HTML' and specifies what the analysis includes (plot of distributions and expert opinions). It distinguishes from sibling tools like 'save_forecast' by focusing specifically on BinaryPredictor predictions, though it doesn't explicitly contrast with all siblings.

  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 includes explicit 'When to use this tool' and 'How to use this tool' sections. It provides clear context: 'when you need to save the BinaryPredictor estimates for the user' and gives specific prerequisites (request_id from previous prediction) and usage steps. This is comprehensive guidance.

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

• create_forecasting_agent_and_get_forecastA
  4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by disclosing key behavioral aspects: it creates an agent (implies stateful operation), specifies the forecast value range (0-1), mentions no historical data requirement, describes how to handle files and large text, and provides guidance on timezone handling and plotting. However, it doesn't mention potential rate limits, error conditions, or performance characteristics.

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

  Conciseness3/5

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

  The description is well-structured with clear sections ('When to use', 'How to use'), but is quite lengthy with multiple paragraphs of detailed instructions. Some information (like plotting guidance) may be better placed elsewhere. While informative, it could be more concise by focusing only on essential guidance for tool invocation rather than downstream usage instructions.

  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 complex tool with 6 parameters, nested objects, and no output schema, the description provides substantial context about what the tool does, when to use it, and how to prepare inputs. It compensates well for the lack of output schema by describing what gets returned (prediction data and text explanation). The main gap is lack of explicit differentiation from the 'reuse_forecasting_agent_and_get_forecast' sibling 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 already documents all parameters thoroughly. The description adds some context about how to think about input_data_model creation and provides examples of field types, but doesn't add significant semantic meaning beyond what's in the schema. The baseline of 3 is appropriate given the comprehensive 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 creates a NormalizedForecaster agent and provides a forecast, with specific mention of returning prediction data and text explanation. It distinguishes from some siblings by focusing on forecasting (vs. prediction or risk assessment), but doesn't explicitly differentiate from 'reuse_forecasting_agent_and_get_forecast' which appears to be a similar tool.

  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 includes explicit 'When to use this tool' section with three specific use cases, including constraints (forecast values between 0 and 1, no historical data required). It also provides a detailed 'How to use this tool' section with step-by-step guidance, though it doesn't explicitly mention when to choose this tool over sibling alternatives like 'reuse_forecasting_agent_and_get_forecast'.

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

• reuse_prediction_agent_and_get_predictionA
  4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and adds significant behavioral context beyond the input schema. It explains the output includes 'consensus prediction', 'individual estimates and text explanations', and 'alpha and beta parameters for a Beta distribution' for confidence intervals. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a prediction tool.

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

  Conciseness2/5

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

  The description is overly verbose and repetitive, with duplicated 'How to use this tool' sections that waste space. While it is well-structured with headings, many sentences could be condensed (e.g., file type lists are excessive). It fails to be front-loaded with critical information, burying key usage guidelines in lengthy instructions.

  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 complexity (3 parameters, nested objects, no output schema, no annotations), the description is moderately complete. It covers purpose, usage, and behavioral output details, but lacks information on error cases, response format specifics, or performance considerations. Without an output schema, more detail on return values would be beneficial.

  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 baseline is 3. The description adds some value by explaining 'agent_id' usage ('The agent is already attached to the correct session'), 'input_data' alignment with 'previously specified input data model', and 'num_experts' context ('minimum and default number is 2', 'up to 30', '2 to 5 experts is sufficient'), but these are mostly clarifications rather than essential semantics beyond the schema.

  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: 'provides prediction input data to a previously created Chronulus BinaryPredictor agent and returns the consensus a prediction from a panel of experts along with their individual estimates and text explanations.' It specifies the verb ('provides', 'returns'), resource ('prediction agent'), and distinguishes it from siblings by emphasizing reuse of existing agents versus creation tools like 'create_prediction_agent_and_get_predictions'.

  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 includes explicit 'When to use this tool' guidance: 'Use this tool to request a prediction from a Chronulus prediction agent that you have already created and when your input data model is unchanged' and 'Use this tool to request a probability estimate from an existing prediction agent in a situation when there is a binary outcome.' It also distinguishes when not to use it ('does not require historical data') and implies alternatives via sibling tool names like creation tools.

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

• create_prediction_agent_and_get_predictionsA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by explaining the creation of an agent, consensus mechanism, expert panel approach, Beta distribution parameters for confidence intervals, and file handling guidelines. It doesn't mention rate limits, authentication needs, or error conditions, keeping it from a perfect score.

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

  Conciseness3/5

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

  The description is well-structured with clear sections (purpose, when to use, how to use) but contains some redundancy and could be more concise. Sentences like 'Remember to pass all relevant information...' could be tightened, and the file type listing is detailed but necessary.

  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 complex tool with 4 parameters, nested objects, and no output schema, the description provides substantial context about the prediction process, expert consensus mechanism, Beta distribution outputs, and file handling. It adequately compensates for the lack of output schema and annotations, though could mention error cases or response format.

  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 baseline is 3. The description adds some context about session_id usage, input_data_model creation, and num_expert guidelines (2-30 range, 2-5 typically sufficient), but doesn't provide significant additional semantics beyond what the schema already documents.

  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 creates a BinaryPredictor agent and provides predictions with consensus from experts, including individual estimates and explanations. It distinguishes from siblings like 'create_forecasting_agent_and_get_forecast' by specifying binary outcomes and no historical data requirement.

  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 explicitly includes 'When to use this tool' section, specifying it's for binary outcome probability estimates without historical data. It distinguishes from alternatives by mentioning specific use cases and provides clear context for when this tool is appropriate.

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

• rescale_forecastA
  4.2/5.0
  Behavior4/5

  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 effectively describes the tool's behavior: it rescales normalized predictions, requires prediction_id and min/max values, handles unit inversion via invert_scale flag, and includes usage caveats about probability scaling. However, it doesn't mention error conditions, rate limits, or authentication needs, which would be helpful for a mutation tool.

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

  Conciseness3/5

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

  The description is structured with clear sections ('When to use this tool', 'How to use this tool'), which is helpful. However, it includes implementation details that don't belong in a tool description (plotting instructions with Rechart, adding notes below explanations). These sentences don't earn their place in a tool definition and make it less concise than ideal.

  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 mutation tool with no annotations and no output schema, the description does a good job covering purpose, usage, and parameters. It explains the transformation behavior and includes important caveats about probability scaling. The main gap is lack of information about return values or error handling, which would be needed for full completeness.

  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 the schema: it mentions presenting prediction_id and min/max as floats, and suggests considering unit inversion for invert_scale. This doesn't significantly enhance understanding beyond what the schema provides, 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.

  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: 'rescales the prediction data (values between 0 and 1) from the NormalizedForecaster agent to scale required for a use case.' It specifies the verb ('rescales'), resource ('prediction data'), and distinguishes from siblings by mentioning the NormalizedForecaster agent, which none of the sibling tools reference. This is specific and well-differentiated.

  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 in a dedicated 'When to use this tool' section. It gives clear conditions for when to use (enough information to determine min/max), when not to use (if min/max are 0 and 1), and alternatives (starting a new Chronulus forecasting use case for better results when converting probabilities to levels). This is comprehensive usage guidance.

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

• reuse_forecasting_agent_and_get_forecastA
  4.2/5.0
  Behavior4/5

  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 effectively describes key behaviors: it creates an agent and returns predictions with explanations, specifies the output range (0 to 1), handles file inputs (images, text, PDFs), manages large text inputs, includes timezone assumptions, and provides plotting guidance. However, it lacks details on error handling, rate limits, or authentication needs, which are common gaps for a tool of this complexity.

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

  Conciseness3/5

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

  The description is well-structured with clear sections ('When to use this tool', 'How to use this tool'), but it is overly verbose at 15+ sentences. Some details, like plotting instructions and timezone assumptions, are tangential to the core tool functionality and could be streamlined. While front-loaded with purpose, it includes unnecessary elaboration that reduces conciseness.

  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?

  Given the tool's complexity (5 parameters, nested objects, no output schema, no annotations), the description provides substantial context: purpose, usage guidelines, behavioral details, and implementation steps. It compensates well for the lack of annotations and output schema by explaining what the tool does and how to use it. However, it could improve by briefly mentioning the return format or error cases to be fully complete.

  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 parameter semantics beyond the schema—it mentions 'agent_id' in the usage guidelines and implies 'input_data' through file handling examples, but doesn't explain parameter interactions or provide additional context. This meets the baseline of 3 when schema coverage is high.

  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: 'creates a NormalizedForecaster agent with your session and input data model and then provides a forecast input data to the agent and returns the prediction data and text explanation from the agent.' It specifies the exact action (create agent, provide forecast input, return prediction and explanation) and distinguishes it from siblings like 'create_forecasting_agent_and_get_forecast' by focusing on reusing an existing agent rather than creating a new one.

  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 includes an explicit 'When to use this tool' section with three bullet points: use for forecasting from Chronulus, specifically for values between 0 and 1 without historical data, and for seasonal weights/probabilities/shares. It also distinguishes from alternatives by implying this is for reuse (vs. creation tools like 'create_forecasting_agent_and_get_forecast'), though it doesn't explicitly name alternatives, 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.

• create_chronulus_sessionA
  4.3/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well: it discloses that the tool creates a session (implying a write operation), returns a unique session_id, and that sessions are reusable under certain conditions. It could improve by mentioning potential errors, rate limits, or authentication needs, but covers key behavioral aspects like creation and reuse.

  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 well-structured with clear sections ('When to use this tool', 'How to use this tool') and front-loaded purpose. It could be more concise by reducing repetition (e.g., 'ask clarifying questions' appears twice) and trimming explanatory details that might be inferred, but overall it's efficient and organized.

  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 creation tool with no annotations and no output schema, the description provides good context: it explains the purpose, usage guidelines, behavioral traits (creation, session_id return, reuse), and parameter expectations. It could be more complete by detailing output format or error handling, but covers most essentials given the complexity.

  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 three parameters (name, situation, task). The description adds some context by explaining that situation is 'broad or background context' and task is 'specific requirements for the forecast', but this largely reiterates schema descriptions. Baseline 3 is appropriate as schema does 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 explicitly states the tool 'creates a new Chronulus Session and returns a session_id', providing a specific verb ('creates') and resource ('Chronulus Session'). It distinguishes this from sibling tools like 'create_forecasting_agent_and_get_forecast' by focusing on session creation rather than forecasting or prediction generation directly.

  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 includes a dedicated 'When to use this tool' section with explicit guidance: use for new forecasting/prediction use cases, ensure sufficient information first, reuse session_id for same situation/task, and create new sessions for different use cases. It also implies alternatives by noting session reuse and distinguishing from other tools that might handle forecasting directly.

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

• save_forecastA
  4.3/5.0
  Behavior4/5

  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 effectively describes key behaviors: it saves files to a specified directory, handles optional rescaling parameters, provides status updates via MCP context, and explains the dual-file output (CSV for data, TXT for explanation). However, it lacks details on error handling or file overwriting policies.

  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 well-structured with clear sections ('When to use this tool', 'How to use this tool'), making it easy to scan. However, some sentences are slightly redundant (e.g., repeating file format requirements), and the overall length could be tightened without losing clarity.

  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 tool with 7 parameters, no annotations, and no output schema, the description does a good job covering purpose, usage, and behavioral context. It explains the tool's role in the workflow and output behavior. However, it lacks details on the output format (e.g., CSV/TXT structure) and error scenarios, leaving some gaps in completeness.

  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 the schema, mainly reiterating parameter purposes (e.g., 'Provide csv_name for the forecast data file') without providing additional context or usage nuances. This meets the baseline for high schema coverage.

  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: 'saves a Chronulus forecast from NormalizedForecaster to separate CSV and TXT files.' It specifies the verb ('saves'), resource ('forecast'), and output formats (CSV and TXT), distinguishing it from sibling tools like 'save_prediction_analysis_html' which outputs HTML instead.

  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 in a dedicated 'When to use this tool' section, listing specific scenarios (e.g., saving both data and explanation files, saving rescaled predictions without prior rescaling). It also implicitly distinguishes from alternatives by not overlapping with sibling tools focused on creation, rescaling, or HTML output.

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