Analytical MCP Server

Server Quality Checklist

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Latest release: v0.4.1
Disambiguation4/5
Most tools have clearly distinct purposes, though advanced_statistical_analysis and analyze_dataset overlap in descriptive stats, and logical_argument_analyzer/logical_fallacy_detector cover similar ground. However, the descriptions explicitly differentiate them, reducing confusion.
Naming Consistency3/5
Naming is inconsistent: some tools use 'advanced_' prefix (advanced_data_preprocessing, advanced_regression_analysis, advanced_statistical_analysis) while others do not. There is a mix of verb-object (analyze_dataset, verify_research) and noun-phrase (decision_analysis, hypothesis_testing) patterns, which lacks uniformity.
Tool Count5/5
12 tools is well-scoped for an analytical server covering statistics, machine learning, logic, and research verification. Each tool addresses a distinct analytical need without feeling excessive or sparse.
Completeness4/5
The tool set covers core analytical tasks including descriptive stats, hypothesis testing, regression, preprocessing, visualization, decision analysis, and research verification. Minor gaps exist (e.g., clustering, time series), but the provided tools form a coherent analytical workflow.
Average 4.5/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 102 commits in the last 12 weeks
- Last stable release on July 6, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly states the tool does not render images, describes the output format, and lists supported types. However, it does not discuss data handling limitations (e.g., size cap of 10000 objects) or any other side effects, missing a chance for deeper transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single sentence that front-loads the primary action ('Generate a chart specification'), distinguishes it from rendering, lists supported types, and states the output. Every word adds value with no redundancy.
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 6 parameters (3 required), no output schema, and nested objects, the description adequately covers purpose and output format. It mentions 'rendering instructions' and 'usage guidance' in the return, but does not explain what these entail or how to interpret the spec. Moderate 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 parameter coverage is 100%, so the baseline is 3. The description does not add new parameter meaning beyond listing supported visualization types, which is already in the schema. No extra details on parameters like 'options' or 'variables' are provided.
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 function: generating a Vega-Lite chart specification and rendering instructions, emphasizing it does not render images. It lists the supported chart types, providing a specific verb and resource. The sibling tools include statistical analysis and modeling, so this tool's visualization focus is distinct.
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 explains the output (markdown report with spec and usage guidance) but provides no explicit guidance on when to use this tool versus siblings like advanced_statistical_analysis or ml_model_evaluation. The lack of 'when not to use' or alternative comparisons reduces clarity.
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?
No annotations exist, so the description carries the burden. It discloses the output format (markdown report with parameters and preview) and mentions data structure handling (flattening from objects). However, it does not discuss side effects, permissions, or whether operations are destructive (though transformations are non-destructive).
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?
Two sentences: first covers purpose and transformations, second covers output and sibling alternative. No filler or repetition.
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 2 parameters and no output schema, the description covers the tool's function, input types, output format, and an alternative tool. It does not discuss performance or limits (though schema has maxItems), but for a simple tool this is adequate.
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 coverage is 100%, baseline 3. The description adds meaning: explains that missing_value_handling drops invalid entries, outlier_detection uses IQR fences, normalization scales to [0,1], standardization gives z-scores, and data can be flattened from objects. This goes 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 it transforms numeric series for modeling, lists four specific transformations (min-max normalization, z-score standardization, missing-value handling, IQR outlier detection), and specifies the output is a markdown report with parameters and preview. It also distinguishes from analyze_dataset.
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?
Explicitly directs to use analyze_dataset for describing data without changing it, providing a clear alternative. While it doesn't specify when not to use this tool beyond that, the guidance is sufficient.
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?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description mentions the output format but does not discuss potential side effects, data handling, or resource implications. Additional context about being a read-only analysis would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences: first covers purpose and output, second provides usage guidelines and alternatives. No redundant information; every sentence adds value.
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?
The description explains the return format and core functionality, but does not mention optional parameters (useTestSplit, includeMetrics, includeCoefficients, etc.) that can alter the output. Given the schema covers these, the description is largely complete but could be slightly more comprehensive.
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 coverage is 100% with descriptions for all parameters. The tool description provides high-level context but adds no specific details about individual parameters beyond what the schema already provides. Baseline score of 3 is appropriate.
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 it fits regression models (linear, polynomial, logistic, multivariate) and returns a markdown report with coefficients, metrics, and interpretation. It also distinguishes itself from siblings: advanced_statistical_analysis for association without a model, and ml_model_evaluation for scoring existing 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?
Explicitly states when to use: 'when you have a designated outcome to predict' and provides specific alternatives for other scenarios. This clear guidance helps the agent select the correct tool.
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?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states the return format (markdown report with test statistic, p-value, conclusion), which is useful but does not address whether the tool is read-only, any side effects, data assumptions, or sample size limits. While basic behavior is clear, more detail would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences that define purpose, list capabilities, and provide usage guidance. It is front-loaded with the core action and output, then details supported tests, and ends with a clear when-to-use note. No wasted words.
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 has no output schema, the description adequately explains the return format (markdown report with test statistic, p-value, conclusion). It covers all supported test types and their data shapes. Missing details like handling of missing data or assumptions, but for a hypothesis testing tool with a clear input schema, this is reasonably complete.
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 input schema covers all 5 parameters (100% coverage), but the description adds value by explaining how the 'data' parameter's shape depends on 'testType' and noting that 'variables' is only used for correlation. This clarifies dynamic behavior beyond the schema's static description, justifying a score above 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 that the tool runs statistical hypothesis tests, enumerates supported test types (t-tests, correlation, chi-square, ANOVA), and specifies the output (p-value with reject/fail-to-reject decision). It also distinguishes itself from the sibling tool advanced_statistical_analysis, which focuses on descriptive correlation without inference.
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 guidance: 'Use this when you need significance; for descriptive correlation without inference use advanced_statistical_analysis.' This directly tells the agent when to use this tool versus an alternative, meeting the highest standard for usage guidelines.
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, the description carries the full burden. It discloses the method ('pattern matching'), output format ('markdown report grouped by category with an overall severity assessment'), and per-fallacy details ('confidence score, description, before/after examples'). It does not mention limitations or false positives, but is otherwise transparent.
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?
Three sentences, no redundancy. First sentence states action and output details, second clarifies format, third gives usage guidance. Every sentence adds value.
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?
Without an output schema, the description adequately explains the return value (markdown report with grouping and severity). Parameters are fully documented in the schema. The sibling mention provides context. Slightly more detail on limitations (e.g., not suitable for non-text inputs) would improve 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 baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides. It mentions output elements (confidence, description, examples) but these are already implied by the parameters 'includeExamples' and 'includeExplanations'.
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 uses specific verbs ('detect and name') and resource ('logical fallacies in text'), and explicitly distinguishes itself from the sibling 'logical_argument_analyzer' by contrasting 'flag specific fallacies' vs 'full argument assessment'.
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?
Provides explicit guidance: 'Use this to flag specific fallacies; for a full argument assessment use logical_argument_analyzer.' This clearly states when to use the tool and when to use the sibling alternative.
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 details return format (markdown report with sample count), metric types per model, and the binary requirement for classification. However, it does not address error handling like mismatched array lengths or non-binary classification inputs.
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?
Three efficient sentences, each serving a distinct purpose: main action, metric details per type, and alternative guidance. No waste, well-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?
Given 4 parameters fully described in schema and no output schema, the description covers return details, alternative tools, and metric computation logic. Minor gap: does not explicitly state array length requirements, though implied by 'same length/order' in schema.
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 coverage is 100%, so baseline is 3. The description adds value by explaining metric families per modelType, default metrics, and that only matching metrics are computed, which goes beyond the schema's simple enum listing.
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 evaluates existing model predictions against actual values, differentiates between classification and regression, and specifies the returned metrics. It also distinguishes from sibling tool advanced_regression_analysis.
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 advises using this tool for scoring supplied predictions and directs to advanced_regression_analysis for fitting models from raw data, providing clear guidance on when to use this vs alternatives.
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, description carries full burden. It discloses requirement for environment variables, live network calls, and failure conditions. Also indicates that 'stakeholder' and 'discipline' shiftTypes are research-backed while others are generic, providing quality insight. Missing details on rate limits or cost, but sufficient for safe invocation.
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?
Three sentences: purpose, requirements+behavior, alternative. Front-loaded with core action and resource. No wasted words.
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 5 parameters, no output schema, and no annotations, description covers purpose, requirements, behavior, and alternatives adequately. Could specify markdown report structure more, but overall complete for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds value by noting which shiftTypes are research-backed vs generic, and confirming currentPerspective is optional context. Does not substantially exceed schema explanation for other parameters.
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?
Clearly states the tool generates alternative viewpoints on a problem via web research, with specific verb 'Generate' and resource 'problem'. Distinguishes from sibling verify_research by contrasting purpose (viewpoints vs. fact-checking).
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 (generating perspectives) and when not to (fact-checking) with direct reference to sibling tool verify_research. Also lists prerequisites (EXA_API_KEY, ENABLE_RESEARCH_INTEGRATION=true) and behavioral constraints (live network calls).
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?
No annotations are provided, so the description carries full burden. It discloses that non-numeric columns are ignored, the return format (markdown), and the correlation strength labels. However, it does not mention handling of missing data or any potential side effects, but for a read-only statistical tool, this is largely sufficient.
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 paragraph that efficiently conveys the purpose, behavior, and usage guidelines without redundant information. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (two parameters, no nested objects), the description covers the return format, handling of non-numeric data, and explicit cross-references to sibling tools. It is fully adequate for an AI agent to understand and invoke correctly.
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 coverage is 100%, but description adds value beyond the schema: it clarifies that 'data' is a table of records, and for 'analysisType' it explains the two modes and their outputs. This enhances understanding beyond the enum labels.
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 it computes per-column descriptive statistics or Pearson correlation for numeric column pairs over a table of records, specifying the return format and distinguishing between the two analysis types. It also differentiates from sibling tools.
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 provides when to use this tool versus alternatives: use analyze_dataset for single series, hypothesis_testing for p-values, and advanced_regression_analysis for predictive models. This is direct and helpful for agent selection.
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?
No annotations provided, but the description discloses the return format (markdown report with summary/stats), the two analysis types, and how it handles arrays of objects. It does not mention error conditions or auth requirements, but for a simple read-only analysis tool this is adequate.
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 three sentences, front-loading the purpose, then detailing output, then usage guidance. Every sentence provides unique value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description covers purpose, input formats, output format, and alternative tools. It is fully sufficient for an agent to select and invoke the tool correctly.
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 100% for both parameters. The description adds extra context beyond the schema: it explains the markdown report content for each analysisType and clarifies that for object arrays, the first numeric property is used.
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 starts with a specific verb and resource: 'Summarize a single numeric series with descriptive statistics.' It clearly distinguishes from siblings by explicitly naming advanced_statistical_analysis and advanced_data_preprocessing for different use cases.
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 alternatives: 'For multi-column tables or cross-variable correlation use advanced_statistical_analysis; to transform values use advanced_data_preprocessing.' This provides clear guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavior: returns a markdown report with ranked options, per-option breakdown (score × weight contribution, strengths, weaknesses), and a recommendation. It also discloses that weights are normalized to sum to 1, providing clear expectations.
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?
Three sentences: first states purpose, second details output, third explains weights. No redundant information, front-loaded with the core action, and efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the output format (markdown report with rankings, breakdown, recommendation) since no output schema exists. It explains the weight normalization and default behavior. Schema covers input constraints (maxItems 100, score range). The tool's purpose is fully addressed without 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?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the score matrix structure ('one row per option, one score (0-10) per criterion') and the behavior of weights (normalization, equal weighting if omitted), which goes beyond the schema descriptions.
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 'Rank options against weighted criteria with a weighted-sum decision matrix', specifying the verb (Rank), resource (options against criteria), and method (weighted-sum matrix). It distinguishes from sibling statistical analysis tools by focusing on decision ranking with criteria weights.
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 indicates when to use: for ranking options against weighted criteria, with a note on omitting weights for equal weighting. It does not explicitly exclude alternatives or mention when not to use, but the context of sibling tools provides sufficient differentiation.
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?
Describes output (markdown analysis) and scope (four aspects plus optional recommendations). No annotations provided; description covers reasonable behavioral context without needing to mention side effects since it's a read-only analysis tool.
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?
Two concise sentences, front-loaded with purpose, no wasted words. Efficiently conveys core functionality and usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 3 parameters and no output schema, description fully covers what the tool does, when to use it, and distinguishes from siblings. Adequate for the tool's complexity.
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 coverage is 100% with descriptions. Description adds context: 'comprehensive' runs all four, and mentions markdown output. Does not deeply explain each parameter beyond schema, but provides useful extra info.
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?
Clearly states the tool assesses structure, validity, strength, and fallacies. Returns a markdown analysis. Differentiates from sibling 'logical_fallacy_detector'.
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 says 'Use this for overall argument quality; to only flag and name fallacies use logical_fallacy_detector.' Provides clear context and alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 makes live network calls, requires specific environment variables, and describes the return structure including cross-source Jaccard consistency and conflict detection. This provides clear understanding of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the primary purpose, followed by return structure, dependencies, and alternative tool. Every sentence provides necessary information without redundancy. It is optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no output schema), the description adequately covers the return structure, prerequisites, and usage context. It provides enough information for an agent to correctly select and invoke the tool.
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 100%, so baseline is 3. The description adds context beyond the schema for some parameters: it clarifies that 'verificationQueries' are alternate phrasings and 'minConsistencyThreshold' is used to mark claims as 'verified'. While not extensive, it adds value.
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: 'Cross-verify a factual claim across multiple web sources via Exa and return a structured confidence verdict.' It uses a specific verb ('cross-verify') and resource ('factual claim'), and distinguishes itself from the sibling tool 'perspective_shifter' by explicitly contrasting use cases.
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 vs. alternatives: 'To generate alternative viewpoints instead of verifying facts, use perspective_shifter.' It also specifies prerequisites: 'Requires EXA_API_KEY and ENABLE_RESEARCH_INTEGRATION=true and makes live network calls; it fails without them.'
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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