xai-toolkit

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but some overlap exists between explain_prediction and explain_prediction_waterfall (both explain individual predictions using SHAP), and between detect_drift and detect_feature_drift (both detect drift, with one being a subset of the other). The descriptions clarify the differences, but an agent might occasionally misselect between these pairs.
Naming Consistency5/5
Tool names follow a consistent snake_case pattern throughout, with clear verb_noun structures (e.g., compare_features, describe_dataset, detect_drift). There are no deviations in naming conventions, making the set predictable and easy to parse.
Tool Count4/5
With 20 tools, the count is slightly high but reasonable for a comprehensive XAI toolkit covering explanation, drift detection, feedback, and context retrieval. It feels slightly heavy but not overwhelming, as each tool appears to serve a specific function in the domain.
Completeness5/5
The toolset provides complete coverage for the XAI domain, including model listing, prediction explanation, drift detection, feedback recording, and business context integration. There are no obvious gaps; it supports a full workflow from model discovery to explanation and evaluation.
Average 3.9/5 across 20 of 20 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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 hints at 'guardrails and checksum' but doesn't explain what these entail—whether they affect performance, security, or data integrity. It doesn't disclose if this is a read-only operation, its rate limits, error handling, or what the output looks like, leaving significant gaps for a tool with parameters.
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 gets straight to the point. It's front-loaded with the core action and resource. However, the vague terms 'guardrails and checksum' add some noise without clear value, slightly reducing effectiveness.
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, 0% schema coverage, no output schema, and 2 parameters, the description is incomplete. It fails to explain key aspects like what a 'skill' is, the meaning of 'guardrails and checksum', expected output, or error scenarios. This is inadequate for a tool that retrieves data by ID/version, as users need more context to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'id/version' but doesn't explain what a 'skill' is, the format of the ID, or what 'version' represents (e.g., semantic versioning, timestamp). The terms 'guardrails' and 'checksum' are not linked to parameters, leaving the two parameters largely undocumented beyond their names.
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 verb 'Retrieve' and the resource 'one skill', specifying it's by 'id/version'. It distinguishes from the sibling 'list_skills' which presumably lists multiple skills. However, it doesn't explicitly contrast with other siblings like 'get_glass_floor' or 'get_taste_context', leaving some ambiguity about the broader toolset context.
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 'guardrails and checksum' but doesn't explain what these are or how they affect usage. There's no mention of prerequisites, error conditions, or comparison with other tools like 'list_skills' for bulk retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It mentions the output format ('ranked list... in plain English') but doesn't disclose behavioral aspects like whether this is a read-only operation, computational cost, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps.
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 efficiently structured: the first sentence states the purpose, the second describes the output format, and the 'Args' section clearly documents parameters. Every sentence adds value with no wasted words.
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 (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and parameters well but lacks behavioral details and usage context, which are important for a feature-ranking tool in a crowded model-analysis ecosystem.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the input schema. While schema description coverage is 0%, the description explains that 'model_id' refers to 'a registered model' with an example, and 'top_n' specifies 'Number of top features to include' with a default. This compensates well for the lack of schema 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: 'Rank features by importance and describe which matter most.' It specifies the verb ('rank') and resource ('features'), though it doesn't explicitly differentiate from sibling tools like 'get_partial_dependence' or 'get_xai_methodology' that might also analyze features.
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. With many sibling tools related to model analysis (e.g., 'explain_prediction', 'get_partial_dependence'), the description lacks context about specific use cases or prerequisites for feature ranking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 describes the return content ('detailed drift analysis') but lacks critical details: it doesn't specify if this is a read-only operation, what permissions are required, whether it's computationally intensive, or if there are rate limits. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 well-structured and concise. The first sentence clearly states the purpose, followed by details on the return value, and then parameter explanations with examples. Every sentence adds value without redundancy, making it easy to parse and understand quickly.
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 (drift analysis with 2 parameters) and lack of annotations and output schema, the description is moderately complete. It covers the purpose and parameters well but misses behavioral aspects like safety, performance, and error handling. Without an output schema, it should ideally describe the return format more thoroughly, though it does list components like 'statistical test results'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for both parameters beyond the input schema, which has 0% description coverage. It explains that 'model_id' refers to 'a registered model' and provides an example ('gbc_lubricant_quality'), and clarifies 'feature_name' as 'Name of the feature to analyze' with an example ('mean radius'). This compensates well for the schema's lack of 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: 'Detect drift for a single feature between training and test data.' It specifies the verb ('detect drift'), resource ('single feature'), and scope ('between training and test data'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'detect_drift' or 'compare_features', which likely serve related purposes.
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 analyzing 'a single feature' but doesn't clarify scenarios where this is preferred over sibling tools like 'detect_drift' (which might analyze multiple features) or 'compare_features'. There are no prerequisites, exclusions, or explicit alternatives stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It states it's a listing operation, implying it's likely read-only and non-destructive, but doesn't confirm this or add behavioral details like pagination, rate limits, authentication needs, or return format. For a tool with zero annotation coverage, 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 with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.
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 has 0 parameters and no output schema, the description is minimally adequate—it states what the tool does. However, with no annotations and many sibling tools, it lacks completeness in behavioral context and usage differentiation, which could hinder an agent's effectiveness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, earning a baseline high score for this dimension.
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 ('List') and the target resource ('available versioned context skills and guardrail metadata'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from its many siblings (like 'list_models' or 'list_drift_alerts'), which would be needed for 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?
No guidance is provided on when to use this tool versus alternatives. With siblings like 'get_skill' (which might fetch a specific skill) and 'list_models' (which lists models), the description lacks context on use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It discloses that the tool returns descriptive statistics in plain English, which is useful behavioral context. However, it doesn't mention potential limitations (e.g., dataset size constraints), error conditions, or performance characteristics like response time or data freshness.
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 well-structured and concise: it starts with the core purpose, lists outputs, and provides parameter details with an example. Every sentence adds value without redundancy, and it's front-loaded with essential 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 the tool's moderate complexity (single parameter, no output schema, no annotations), the description is adequate but could be more complete. It explains what the tool does and the parameter, but lacks details on output format (beyond 'plain English'), error handling, or dependencies on model registration status. For a tool with no structured output schema, more detail on return values would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It provides a clear explanation of the single parameter 'model_id' with an example ('gbc_lubricant_quality'), adding meaningful context beyond the schema's basic string type. This adequately covers the parameter semantics for this simple tool.
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: 'Describe the dataset associated with a model' with specific outputs listed (number of samples, features, etc.). It distinguishes from siblings by focusing on dataset description rather than model comparison, drift detection, or explanation. However, it doesn't explicitly contrast with similar tools like 'summarize_model' or 'list_models'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing dataset insights for a specific model, as shown by 'associated with a model' and the model_id parameter. However, it doesn't explicitly state when to use this tool versus alternatives like 'summarize_model' or 'list_models', nor does it provide exclusion criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool is 'retrieval-first' and doesn't run SHAP/drift computations, which is useful behavioral context. However, it lacks details on permissions, rate limits, output format, or error handling, leaving gaps for a tool with parameters.
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 appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first defines the tool, the second clarifies behavior, the third sets context, and the parameter explanations are concise and necessary. Zero wasted content.
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, behavior, and parameters well, but lacks details on return values, error cases, or dependencies, which are important for a tool with multiple parameters and sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all three parameters: model_id and run_id as optional filters, and top_cases as the maximum number of highlighted samples. This goes beyond the schema's basic titles, though it could provide more detail on format or constraints.
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 generates a predefined briefing from persisted batch results, specifying it's retrieval-first and doesn't run computations. It distinguishes itself from siblings by focusing on reusable briefings rather than analysis, comparison, or listing functions, 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('reusable daily/weekly briefing entry point for stakeholders who ask a similar set of baseline questions') but doesn't explicitly state when to use this tool versus alternatives like list_models or summarize_model. No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It mentions the tool generates a plot but doesn't disclose behavioral traits such as output format (e.g., image file, interactive plot), performance considerations, or error handling. This leaves gaps for an agent to understand how to invoke and interpret results.
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 front-loaded with the core purpose, followed by a brief explanation of the waterfall plot, and then parameter details. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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 incomplete for a tool that generates visualizations. It explains the purpose and parameters but lacks details on output behavior (e.g., how the plot is returned), which is critical for an agent to use it effectively. The complexity of SHAP analysis warrants more contextual information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining that 'model_id' refers to 'a registered model' with an example, and 'sample_index' is a 'row index in the test dataset to explain (0-based).' This clarifies parameter purposes beyond the schema's basic types, though it could provide more context on valid ranges or formats.
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 specific action ('Show a SHAP waterfall plot') and resource ('for a single prediction'), distinguishing it from sibling tools like 'explain_prediction' (likely more general) and 'get_xai_methodology' (methodological). It specifies this is 'the most detailed SHAP visualization,' which helps differentiate its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for detailed single-prediction explanations but doesn't explicitly state when to use this tool versus alternatives like 'explain_prediction' or 'compare_predictions.' It mentions the tool's detailed nature but lacks explicit guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 describes the return data (model IDs, types, etc.) and implies a read-only operation, but doesn't cover aspects like rate limits, pagination, or error handling. This is adequate but has clear gaps for a tool with zero annotation coverage.
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 appropriately sized and front-loaded, with two concise sentences that each serve a clear purpose: the first explains what the tool does and returns, and the second provides usage guidance. There's no wasted text.
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 (simple list operation with 0 parameters) and the lack of annotations and output schema, the description is minimally complete. It covers the purpose and usage but lacks details on behavioral traits like performance or limitations. This is adequate but leaves room for improvement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter information, so it meets the baseline expectation. No additional value is required, but it doesn't exceed expectations either.
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 ('List') and resource ('all registered models with their metadata'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this tool from its sibling tools (like 'list_drift_alerts' or 'list_skills'), which would be needed for 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool ('Use this to discover what models are available before asking questions'), which helps guide the agent. It doesn't explicitly state when not to use it or name specific alternatives among siblings, so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 discloses key behavioral traits: it explains the prediction process (using SHAP values), includes fallback behavior (checks result store first, then computes on-the-fly), and mentions optional outputs (base64 PNG chart). However, it lacks details on permissions, rate limits, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first states the goal, the second details the output, and the third explains the computation process. Minor verbosity in the args section could be trimmed, but overall it's efficient.
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 for a 3-parameter tool. It covers purpose, parameters, and behavioral aspects but lacks details on return values (e.g., narrative structure), error cases, or performance implications. For a tool with computational fallback, more context on limitations would help.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's role: 'model_id' identifies a registered model, 'sample_index' is a row index in the test dataset, and 'include_plot' controls visualization output. This clarifies semantics beyond the bare schema, though it could specify format constraints (e.g., model_id examples).
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 ('explain why', 'returns a plain-English narrative') and resources ('a single sample', 'classification', 'features', 'SHAP values'), distinguishing it from siblings like 'explain_prediction_waterfall' by focusing on narrative explanations with optional visualizations rather than waterfall plots.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by mentioning it explains 'a single sample' and checks for 'precomputed explanations', but it does not explicitly state when to use this tool versus alternatives like 'explain_prediction_waterfall' or 'get_xai_methodology'. No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 adds useful context: it's a 'discovery tool' that returns 'precomputed drift results' and does not 'fall back to on-the-fly computation,' which clarifies its read-only and static nature. However, it doesn't mention potential limitations like rate limits, authentication needs, or what happens if no data exists, leaving gaps in transparency for a tool with no annotations.
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 appropriately sized and front-loaded: the first sentence states the purpose, the second adds behavioral context, and the Args section efficiently details parameters. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.
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, no annotations, no output schema), the description is somewhat complete but has gaps. It covers purpose, usage context, and parameter semantics well. However, without annotations or an output schema, it doesn't describe return values, error handling, or other behavioral traits like data freshness or access permissions, which are important for a discovery tool with no structured safety hints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explains each parameter's purpose: model_id filters to a specific model or all models, severity filters by levels ('none', 'moderate', 'severe'), and run_id filters to a specific batch run. This compensates fully for the schema's lack of descriptions, providing clear semantics for all three 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: 'Browse batch drift findings across features and models' and 'Returns precomputed drift results from the result store.' It specifies the verb ('browse'), resource ('drift findings'), and scope ('across features and models'), making it distinct from siblings like detect_drift or detect_feature_drift. However, it doesn't explicitly differentiate from all siblings, such as list_models or list_skills, which slightly limits its clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage: 'This is a discovery tool — it does NOT fall back to on-the-fly computation.' This implicitly suggests using it for precomputed results and not for real-time detection, distinguishing it from tools like detect_drift. However, it lacks explicit when-not-to-use scenarios or named alternatives, such as comparing to detect_drift for on-the-fly computation, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It discloses the return format ('model type, accuracy, number of features, and the top features ranked by importance — all in plain English'), which is helpful behavioral context. However, it does not mention potential limitations like model availability, permissions needed, or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by return details and parameter explanation in a clear, bullet-like structure. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 moderate complexity (single parameter, no output schema, no annotations), the description is largely complete: it covers purpose, returns, and parameter meaning. However, it lacks details on error handling or dependencies, which would be beneficial for robust agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter: it explains that model_id refers to 'ID of a registered model' and provides an example ('gbc_lubricant_quality'). Since schema description coverage is 0% (no schema descriptions exist), this compensates well, though it could specify format constraints like length or allowed characters.
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 specific action ('Summarize what a model does and what drives its decisions') and the resource ('a model'), distinguishing it from siblings like list_models (which lists models) or explain_prediction (which explains specific predictions). It provides a concrete verb and target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a high-level overview of a model's characteristics and feature importance, but it does not explicitly state when to use this tool versus alternatives like get_skill (for skill metrics) or explain_prediction (for individual predictions). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 valuable behavioral context: it discloses the two-step process (checking result store, then computing), the statistical tests used (PSI, KS, chi-squared), and the fallback mechanism. However, it doesn't mention permissions, rate limits, or output format details.
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 front-loaded with the core purpose, followed by implementation details and parameter explanation in a structured 'Args' section. Every sentence earns its place without redundancy, making it highly efficient and easy to parse.
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?
For a tool with no annotations, no output schema, and low schema coverage, the description does well on purpose and behavior but lacks details on output format, error handling, and prerequisites. It's adequate for basic use but leaves gaps for an agent to fully understand execution and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, but the description compensates by explaining the single parameter 'model_id' with an example ('gbc_lubricant_quality') and clarifying it refers to a registered model. This adds meaningful semantics beyond the bare schema, though it doesn't detail format constraints or validation rules.
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 ('detect data drift') and resources ('between a model's training data and test data'), distinguishing it from siblings like 'detect_feature_drift' by focusing on overall model drift rather than feature-specific analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for drift detection in registered models but provides no explicit guidance on when to use this tool versus alternatives like 'detect_feature_drift' or 'list_drift_alerts'. It mentions checking the result store first, which hints at performance considerations, but lacks clear when/when-not directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 helpfully clarifies this is a 'discovery tool' that doesn't perform computation, which is valuable behavioral context. However, it doesn't mention other important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what format the summary returns.
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 perfectly structured and concise. It starts with the core purpose, explains what it returns, clarifies what it doesn't do, and then provides parameter semantics. Every sentence earns its place with no wasted words, and the information is front-loaded appropriately.
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?
For a discovery/list tool with 2 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the purpose and parameter meanings well, but lacks information about the return format, pagination, error conditions, or authentication requirements that would be helpful for an AI agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It provides meaningful context for both parameters: 'model_id' as 'Model identifier' and 'run_id' as 'Filter to a specific batch run'. This adds valuable semantic understanding beyond the bare schema, though it doesn't specify format examples or constraints for these identifiers.
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 ('browse', 'returns a summary') and resources ('samples with precomputed explanations', 'result store'). It explicitly distinguishes itself from computation tools by stating 'it does NOT compute explanations on the fly', which differentiates it from sibling tools like explain_prediction or explain_prediction_waterfall.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: for discovery of precomputed explanations rather than on-the-fly computation. However, it doesn't explicitly mention when NOT to use it or name specific alternative tools from the sibling list, though the distinction from computation tools is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It discloses that the tool returns aggregated feedback and that all filters are optional (which implies default behavior when omitted). However, it doesn't describe important behavioral aspects like response format, pagination, rate limits, authentication requirements, or whether this is a read-only 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 efficiently structured with purpose first, usage context second, example third, and parameter details last. Every sentence adds value - no redundant or wasted words. The parameter explanations are clear and direct without unnecessary elaboration.
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?
For a 4-parameter tool with no annotations and no output schema, the description does well on purpose and parameters but lacks important behavioral context. It doesn't describe the return format, error conditions, or operational constraints. The example helps but doesn't fully compensate for the missing structural information about what the tool actually returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 4 parameters. Each parameter gets a specific explanation of what it filters for, with concrete examples ('reliability_engineer', 'lubricants', 'explain_prediction'). The description also clarifies that all filters are optional and what happens when omitted.
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 ('retrieve organizational taste', 'understand audience preferences') and distinguishes it from siblings by focusing on aggregated expert feedback about explanation quality. It explains what 'taste' means in this context (what experts think good explanations look like).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('before presenting results' to 'understand audience preferences') and gives a concrete example of how the feedback can be applied. However, it doesn't explicitly contrast when to use this versus specific sibling tools like 'retrieve_business_context' or 'get_skill'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 key behavioral traits: the tool returns agreement status, confidence levels, and feature analysis in plain English. However, it does not mention potential limitations (e.g., model availability, sample range), error handling, or performance characteristics like rate limits, leaving some gaps.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by return details and usage rationale, with a clear 'Args' section. Every sentence adds value without redundancy, making it efficient and well-structured.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is largely complete: it covers purpose, parameters, and return values. However, without an output schema, it could benefit from more detail on the return format (e.g., structure of the plain English explanation), slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema by explaining each parameter's purpose with examples (e.g., model IDs like 'gbc_lubricant_quality', sample index as 'row index in the test dataset'), clarifying their roles in the comparison process effectively.
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 ('compare', 'explain') and resources ('what two models predict for the same sample'). It distinguishes from siblings like 'explain_prediction' (single model) and 'compare_features' (features rather than predictions), making the scope explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage ('to build trust in predictions by checking cross-model consistency'), but does not explicitly state when not to use it or name specific alternatives among siblings. It implies usage for model comparison rather than other tasks, but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 key behavioral aspects: it returns a narrative description, optionally includes a visualization (PDP+ICE plot as base64 PNG), explains what PDP and ICE represent (average effect vs individual sample effects), and clarifies the visualization is model-agnostic. It doesn't mention rate limits, authentication needs, or data size constraints, but provides substantial operational 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 efficiently structured with zero wasted sentences. It starts with the core purpose, then describes the return value, then explains optional visualization, then clarifies the visualization components (PDP vs ICE), and finally documents all parameters clearly. Every sentence adds essential information.
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 3-parameter tool with no annotations and no output schema, the description provides excellent coverage of inputs, behavior, and output format. It explains what the tool returns (narrative + optional plot), how the visualization works, and all parameter meanings. The only minor gap is not explicitly describing the narrative format or potential error conditions, but overall it's highly complete given the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 3 parameters: 'model_id' is described as 'ID of a registered model' with an example, 'feature_name' as 'Name of the feature to analyze' with an example, and 'include_plot' as controlling whether to include a PDP+ICE plot as base64 PNG with default value. Each parameter's purpose and format is clearly explained beyond the bare 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 with specific verbs ('Show how a single feature affects predictions', 'Returns a narrative describing the relationship') and distinguishes it from siblings by specifying it's about partial dependence analysis (not SHAP-based, not feature comparison). It explicitly names the resource (model feature) and output type (narrative + optional plot).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through phrases like 'model-agnostic visualization, not SHAP-based' which differentiates it from SHAP-based explanation tools, but doesn't explicitly state when to use this versus alternatives like 'explain_prediction' or 'compare_features'. No explicit when-not-to-use guidance or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 the tool's return value ('two-layer separation protocol') and its purpose in explanation presentation, but doesn't address potential limitations, error conditions, or implementation details that would help an agent understand behavioral traits.
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?
Perfectly concise with two sentences that each serve distinct purposes - first states the tool's purpose, second provides usage guidance. No wasted words or redundant information.
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 zero-parameter tool with no annotations and no output schema, the description provides good context about what the tool returns and when to use it. However, without an output schema, more detail about the 'two-layer separation protocol' structure would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's purpose and usage.
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 ('Retrieve') and resources ('Glass Floor separation principles'), and distinguishes it from siblings by focusing on explanation presentation protocols rather than model analysis or data 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?
Explicitly states when to use the tool ('when you need to present both deterministic model outputs and AI-interpreted business guidance') and provides clear context about its specific application scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 narratives are hashed for auditability (a behavioral trait) and lists valid rating values, but doesn't mention whether this is a write operation (implied by 'Record'), permission requirements, rate limits, or what happens after recording. It adds some context 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 in the first sentence. Each subsequent sentence adds necessary information about hashing, valid ratings, and parameter explanations. While efficient, the parameter section could be slightly more structured (e.g., bullet points) but remains clear.
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 7 parameters with no schema descriptions and no annotations, the description does an excellent job explaining parameter semantics and usage context. However, it lacks information about what happens after recording (no output schema) and doesn't address potential error conditions or system behavior. For a tool with this complexity, it's nearly complete but has minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations. It explains what each parameter represents (e.g., 'model_id: Which model produced the output'), lists valid rating values, specifies which parameters are optional, and clarifies that narrative text will be hashed. This adds substantial meaning beyond the bare 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 specific action ('Record expert feedback') on a specific resource ('toolkit narrative'), distinguishing it from sibling tools like 'explain_prediction' or 'list_models' which serve different purposes. It provides a concrete verb-resource pair with additional context about hashing for auditability.
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 states when to use this tool: 'Call this when an expert evaluates the quality of an explanation.' This provides clear context for invocation, though it doesn't specify when NOT to use it or name alternatives among siblings. The guidance is direct and sufficient for proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 and discloses key behavioral traits: it's a retrieval operation (implied read-only), returns a comprehensive guide, and emphasizes its preparatory role. It doesn't mention permissions, rate limits, or response format, leaving some gaps.
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 front-loaded with the core purpose, followed by usage timing and content details in two efficient sentences. Every sentence adds value, with no wasted words or unnecessary elaboration.
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 (foundational guide) and lack of annotations/output schema, the description is mostly complete: it covers purpose, usage, and content. However, it doesn't specify the return format (e.g., text, structured data) or potential errors, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema coverage, so no parameter information is needed. The description appropriately adds context about the guide's content (workflow, protocol, anti-patterns) without redundant parameter details, exceeding the baseline of 3.
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 a specific verb ('Retrieve') and resource ('XAI analysis methodology guide'), distinguishing it from siblings like 'get_glass_floor' or 'standard_briefing' by focusing on the full workflow guide rather than specific components.
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?
It explicitly states when to use this tool ('BEFORE starting any model explanation') and provides context about its role in understanding tool sequences, protocols, and anti-patterns, offering clear guidance without alternatives needed for a foundational resource.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 behavioral traits: the search scope ('loaded business documents'), return format ('ranked chunks with source provenance'), presentation pattern ('Glass Floor presentation pattern'), and important limitations ('provenance_label is always ai-interpreted', 'any synthesis... is NOT deterministic'). It doesn't mention rate limits or authentication needs, but covers most critical behavioral aspects.
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 well-structured and appropriately sized. It begins with the core purpose, then explains the search behavior, specifies usage guidelines with examples, and concludes with important behavioral notes. Every sentence adds value, with no redundant information or unnecessary elaboration.
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 no annotations and no output schema, the description provides strong contextual completeness. It covers purpose, usage guidelines, behavioral traits, and parameter semantics effectively. The main gap is the lack of information about the exact structure of returned chunks, but given the complexity level and the clear explanation of what will be returned ('ranked chunks with source provenance'), this is a minor omission.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must fully compensate for the lack of parameter documentation in the schema. It successfully provides clear semantics for both parameters: 'query' is explained as 'Natural language search query' with examples, and 'top_k' is explained as 'Maximum number of chunks to return' with its default value. The description adds substantial value beyond the bare 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 with specific verbs ('retrieve relevant business context', 'searches loaded business documents') and resources ('knowledge base', 'business documents like clinical protocols, operational rules'). It distinguishes itself from siblings by focusing on business context retrieval rather than model explanation, comparison, or listing functions.
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 ('Use this AFTER an explainability tool to find actionable business guidance') and gives a concrete example with a specific sibling tool ('after explain_prediction returns...'). It also specifies the intended workflow context clearly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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