The Analytical MCP Server provides 12 analytical tools for statistical analysis, machine learning evaluation, decision support, logical reasoning, and research verification.
Statistical Analysis
Descriptive Statistics: Summarize a numeric series (count, min, max, mean, median, quartiles, std dev, variance, etc.) or compute per-column stats and Pearson correlation for tabular data.
Regression Analysis: Fit linear, polynomial, logistic, or multivariate regression models with coefficients, performance metrics, and interpretation.
Hypothesis Testing: Run Welch's t-test, paired t-test, Pearson correlation significance, chi-square independence, or one-way ANOVA with exact p-values.
Data Preprocessing: Apply min-max normalization, z-score standardization, missing-value handling, or IQR outlier detection.
Data Visualization: Generate Vega-Lite chart specifications for scatter, line, bar, histogram, box, heatmap, pie, violin, and correlation plots.
Machine Learning
Model Evaluation: Score predictions using classification metrics (accuracy, precision, recall, F1) or regression metrics (MSE, MAE, RMSE, R²).
Decision Support
Decision Analysis: Rank options against weighted criteria using a weighted-sum decision matrix, with per-option breakdowns, strengths/weaknesses, and a recommendation.
Logical Reasoning
Argument Analysis: Assess argument structure, validity, strength, and fallacies, with optional improvement recommendations.
Fallacy Detection: Identify logical fallacies in text with confidence scores, descriptions, and examples.
Perspective Shifting: Generate alternative viewpoints (stakeholder, discipline, contrarian, optimistic, pessimistic) grounded in live web research. (Requires EXA_API_KEY)
Research Verification
Research Verification: Cross-verify factual claims across multiple web sources, returning a structured confidence verdict with consistency scoring and conflict detection. (Requires EXA_API_KEY)
Observability
Built-in Prometheus-style metrics server (port 9090) exposing cache performance, uptime, memory, and CPU usage.
Integrated for environment variable management, allowing configuration of API keys, feature flags, cache settings, NLP and server settings through a .env file
Integrated for testing capabilities, supporting various test suites including integration tests, Exa Research API tests, and server tool registration tests
Required as the runtime environment for the MCP server, specifically version 20 or higher
Integrated for package management, handling dependencies and running scripts for building, testing, and demonstrating the server's capabilities
Core technology for implementing the server, providing type safety for analytical tools and NLP capabilities
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Analytical MCP Server analyze this sales dataset for trends and outliers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol server exposing 12 analytical tools for Claude: descriptive statistics, regression (linear/polynomial/logistic/multivariate), hypothesis testing, data preprocessing, data visualization specs, multi-criteria decision analysis, ML model evaluation, logical argument/fallacy analysis, and Exa-backed research verification. Runs over stdio; the analytical core needs no API key (research features require EXA_API_KEY).
Node.js >= 20.0.0
EXA_API_KEY environment variable (required for verify_research and perspective_shifter, both of which call the Exa search API on every invocation)
npm install
npm run buildBuild the image. The server speaks the MCP protocol over stdio — it is launched (and its stdin/stdout piped) by the MCP client, not run as a detached daemon; see the Docker entry under Configuration for how Claude Desktop invokes it.
docker build -t analytical-mcp .
# Smoke-test the image interactively (Ctrl-C to exit):
docker run --rm -i -e EXA_API_KEY=your_api_key_here analytical-mcpCopy .env.example to .env
Add your EXA_API_KEY to .env
Add to Claude Desktop configuration:
{
"mcpServers": {
"analytical": {
"command": "node",
"args": ["/path/to/analytical-mcp/build/index.js"],
"env": {
"EXA_API_KEY": "your-exa-api-key-here"
}
}
}
}Copy .env.example to .env
Add your EXA_API_KEY to .env
Add to Claude Desktop configuration:
{
"mcpServers": {
"analytical": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"--env-file", ".env",
"-v", "$(pwd)/cache:/app/cache",
"analytical-mcp"
]
}
}
}Related MCP server: MCP Reasoner
The server registers 12 tools on startup, unconditionally (registration does not depend on EXA_API_KEY; the two research-backed tools below will error at call time if the key is missing). See src/tools/index.ts for the authoritative list.
analyze_dataset — Descriptive statistics for a numeric or record-array dataset (summary/stats).
advanced_statistical_analysis — Descriptive statistics plus cross-variable Pearson correlation on tabular data (arrays of objects). Use analyze_dataset for a single numeric series.
advanced_regression_analysis — Linear, polynomial, logistic, and multivariate regression, backed by dedicated provider modules with real OLS/logistic math (not mocked).
hypothesis_testing — Real statistical hypothesis tests: Welch's independent t-test, paired t-test, correlation, chi-square, and ANOVA, using exact p-value computation (see src/utils/statistics.ts).
advanced_data_preprocessing — Normalization, standardization, missing-value handling, and IQR outlier detection on numeric data.
data_visualization_generator — Generate chart specifications (scatter, line, bar, histogram, box, heatmap, pie, violin, correlation).
ml_model_evaluation — Evaluate model predictions: classification metrics (accuracy, precision, recall, F1) or regression metrics (MSE, MAE, RMSE, R²).
decision_analysis — Multi-criteria weighted decision ranking. Requires a scores matrix (options.length rows × criteria.length columns, each value 0-10) in addition to options and criteria; weights is optional and defaults to equal weighting. This is a breaking requirement versus older docs that only described options/criteria/weights.
logical_argument_analyzer — Analyze argument structure, fallacies, validity, and strength (via dedicated provider classes).
logical_fallacy_detector — Detect and explain logical fallacies in text with confidence scoring.
perspective_shifter — Generate alternative perspectives (stakeholder, discipline, contrarian, optimistic, pessimistic) on a problem. Requires EXA_API_KEY: it runs an Exa search per perspective domain to ground each perspective.
verify_research — Cross-verify research claims from multiple sources. Requires EXA_API_KEY. Returns confidence.score (the actual computed consistency/confidence value, 0-1) and confidence.verified (boolean: whether confidence.score met minConsistencyThreshold) — the threshold is a pass/fail cutoff, never a floor applied to the reported score.
The Analytical MCP Server includes a built-in Prometheus-style metrics HTTP server (src/utils/metrics_server.ts) for monitoring cache performance and system health.
When enabled, the server exposes metrics via HTTP on port 9090 (configurable):
http://localhost:9090/metrics - Prometheus-style metrics
http://localhost:9090/metrics?format=json - JSON format metrics
http://localhost:9090/health - Health check endpoint
http://localhost:9090/ - Metrics server status page
analytical_mcp_cache_hits_total - Cache hits by namespace
analytical_mcp_cache_misses_total - Cache misses by namespace
analytical_mcp_cache_puts_total - Cache puts by namespace
analytical_mcp_cache_evictions_total - Cache evictions by namespace
analytical_mcp_cache_size - Current cache size by namespace
analytical_mcp_uptime_seconds - Server uptime in seconds
analytical_mcp_memory_usage_bytes - Memory usage (RSS, heap, external)
analytical_mcp_cpu_usage_microseconds - CPU time usage (user, system)
Enable metrics by setting environment variables:
METRICS_ENABLED=true # Enable metrics server (default: false; unauthenticated, opt-in)
METRICS_PORT=9090 # Metrics server port (default: 9090)
METRICS_HOST=127.0.0.1 # Metrics server host (default: 127.0.0.1, use 0.0.0.0 to bind to all interfaces)# Get Prometheus metrics
curl http://localhost:9090/metrics
# Get JSON metrics
curl http://localhost:9090/metrics?format=json
# Health check
curl http://localhost:9090/healthEvery tool call emits one structured audit record to stderr (never stdout — that is the MCP
protocol channel), independent of LOG_LEVEL:
[2026-07-05T22:56:12.629Z] AUDIT: {"event":"tool_call","tool":"verify_research","ok":true,"durationMs":1352,"argBytes":120,"argHash":"31b769fe1f66","exaCalls":2}Each record carries the tool name, outcome, duration, the byte size and a SHA-256 fingerprint
of the arguments (never the raw argument values, so no content is leaked), and exaCalls — the
number of outbound Exa requests the call issued. This gives an operator a forensic trail (oversized
or repeated inputs, unexpected external fan-out) without recording sensitive content. It is gated
by a single flag, on by default and independent of LOG_LEVEL:
ENABLE_AUDIT_LOG=true # per-call audit records to stderr (default: true){
"data": [23, 45, 67, 12, 89, 34, 56, 78],
"analysisType": "stats"
}{
"options": ["Option A", "Option B", "Option C"],
"criteria": ["Cost", "Quality", "Speed"],
"scores": [
[7, 6, 8],
[5, 9, 6],
[9, 4, 7]
],
"weights": [0.4, 0.4, 0.2]
}{
"testType": "t_test_independent",
"data": [[23, 45, 67, 12, 89], [34, 56, 78, 90, 21]],
"alpha": 0.05
}{
"argument": "All birds can fly. Penguins are birds. Therefore, penguins can fly.",
"analysisType": "comprehensive"
}# Run the offline unit suite (no API key needed) — same as test:unit
npm test
# Unit tests only (offline, no API key needed)
npm run test:unit
# Integration tests (non-blocking heads-up without EXA_API_KEY; live-Exa cases self-skip)
npm run test:integration
# Integration tests excluding the live-API suite
npm run test:integration:no-api
# Or via the thin wrapper script
./tools/test-runner.sh unit
./tools/test-runner.sh integration
./tools/test-runner.sh integration:no-apinpm run build - Build TypeScript to JavaScript
npm run watch - Watch for changes and rebuild
npm run typecheck - Type-check src/ (excludes test files)
npm run typecheck:src - Type-check src/ plus integration tests
npm run lint / npm run lint:fix - ESLint
npm run format / npm run format:check - Prettier
npm test / npm run test:unit / npm run test:integration - Jest (see Testing)
npm run smoke - Builds, starts the real server, and drives initialize/tools-list/tools-call over stdio JSON-RPC
npm run cache:stats / cache:clear / cache:preload - Manage the on-disk research cache
npm run inspector - Start MCP inspector for debugging
analytical-mcp/
├── src/
│ ├── tools/ # MCP tool implementations (12 registered tools + supporting providers)
│ ├── utils/ # Utility functions, regression/NLP providers, caching, resilience, metrics
│ ├── integration/ # Integration tests (live-Exa cases self-skip without EXA_API_KEY)
│ ├── __tests__/ # Server-level protocol test (InMemoryTransport)
│ └── index.ts # Main server entry point
├── docs/ # Documentation
├── tools/ # Development and testing scripts
├── scripts/ # Build/smoke-test scripts
└── examples/ # Usage examplesProvider architecture: Complex tools (regression, NLP, visualization, argument analysis) are decomposed into single-responsibility provider modules in src/utils/ and src/tools/ (e.g. linear_regression_provider.ts, logistic_regression_provider.ts, polynomial_regression_provider.ts, multivariate_regression_provider.ts, regression_metrics_provider.ts). Tool files orchestrate and format; providers hold the logic.
Resilience: src/utils/rate_limit_manager.ts handles Exa rate limiting (key rotation, per-endpoint throttling); src/utils/api_helpers.ts provides retry with an explicit shouldRetry predicate.
Caching: src/utils/cache_manager.ts, src/utils/enhanced_cache.ts, and src/utils/research_cache.ts provide layered, namespace-aware caching (enable with ENABLE_RESEARCH_CACHE=true).
Statistics: src/utils/statistics.ts implements log-gamma, incomplete beta/gamma, and t/F/chi-square CDFs from first principles for exact p-value computation — no statistical approximations or mocked results.
Descriptive statistics: mean, median, standard deviation, quartiles
Regression analysis: linear, polynomial, logistic, multivariate
Hypothesis testing: Welch t-test, paired t-test, correlation, chi-square, ANOVA
Multi-criteria weighted decision ranking from an explicit options × criteria score matrix
Argument structure, validity, and strength analysis
Fallacy detection with confidence scoring
Perspective generation
Multi-source verification via Exa
Fact extraction
Conflict/consistency checking
Confidence scoring
All analytical processing occurs locally
Research features use the Exa API (optional, requires EXA_API_KEY)
No permanent data storage beyond the optional local disk cache
API keys managed via environment variables
MIT License. See LICENSE file for details.
See CONTRIBUTING.md for the contribution workflow and conventions, and docs/DEVELOPMENT.md for the full development reference. All participants are expected to follow the Code of Conduct.
In short: branch from main, make sure npm run typecheck, npm run lint, npm test, and
npm run smoke all pass, add tests for new behavior, and open a PR using the template.
To report a security vulnerability, follow SECURITY.md — do not open a public issue.
JSON parsing errors: All logging must go to stderr, not stdout. MCP protocol uses stdout for communication. Use the Logger class, not console.log.
Tools not appearing: Verify server configuration in Claude Desktop settings and restart Claude Desktop application.
Research features fail at call time: Set EXA_API_KEY in your environment or .env file — verify_research and perspective_shifter both require it even though all 12 tools register regardless of whether it is set.
Server not starting: Check Node.js version is 20 or higher and all dependencies are installed with npm install.
See docs/TROUBLESHOOTING.md for detailed troubleshooting guidance.
Start the server with the MCP inspector:
npm run inspectorTransform a numeric series for downstream modeling: min-max normalization, z-score standardization, missing-value handling, or IQR outlier detection. Returns a markdown report with the transform's parameters and a preview of the resulting values. Use analyze_dataset to describe data without changing it.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The numeric series to transform: a number[], or an array of {key: number} records (values are flattened). | |
| preprocessingType | Yes | 'normalization' (scale to [0,1]), 'standardization' (z-scores), 'missing_value_handling' (drop invalid/missing entries), or 'outlier_detection' (flag values outside the IQR fences). |
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.
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.
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.
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.
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.
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.
Fit a regression model (linear, polynomial, logistic, or multivariate) predicting a named dependent variable from named predictor columns. Returns a markdown report with fitted coefficients, performance metrics, and interpretation. Use this when you have a designated outcome to predict; for association strength without a model use advanced_statistical_analysis, and to score existing predictions use ml_model_evaluation.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of data points for regression analysis | |
| useTestSplit | No | Whether to use train/test split (default: false) | |
| includeMetrics | No | Whether to include performance metrics (default: true) | |
| regressionType | Yes | Type of regression analysis to perform | |
| polynomialDegree | No | Degree for polynomial regression (2-6, default: 2) | |
| dependentVariable | Yes | Name of dependent variable (response) | |
| includeCoefficients | No | Whether to include coefficient details (default: true) | |
| independentVariables | Yes | Names of independent variables (predictors) | |
| standardizeVariables | No | Whether to standardize variables (default: false) | |
| useConfidenceInterval | No | Whether to include confidence intervals (default: false) |
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.
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.
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.
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.
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.
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.
Compute per-column descriptive statistics, or Pearson correlation for every numeric column pair, over a table of records. Returns a markdown report (mean/median/std/variance/min/max per column, or r plus a weak/moderate/strong label per pair); non-numeric columns are ignored. Use analyze_dataset for a single numeric series; for correlation significance (p-values) use hypothesis_testing; to fit a predictive model use advanced_regression_analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of data objects for statistical analysis | |
| analysisType | Yes | 'descriptive' for per-column summary statistics, or 'correlation' for Pearson r across all numeric column pairs. |
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.
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.
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.
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.
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.
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.
Summarize a single numeric series with descriptive statistics. Returns a markdown report: 'summary' gives count/min/max/mean/sum; 'stats' adds median, quartiles, standard deviation, variance, and coefficient of variation. Accepts a number[] or an array of objects (the first numeric property is used). For multi-column tables or cross-variable correlation use advanced_statistical_analysis; to transform values use advanced_data_preprocessing.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The numeric series to summarize: a number[], or an array of objects whose first numeric property is analyzed. | |
| analysisType | No | 'summary' (count/min/max/mean/sum, default) or 'stats' (adds median, quartiles, standard deviation, variance, coefficient of variation). | summary |
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.
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.
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.
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.
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.
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.
Generate a chart specification (Vega-Lite) plus rendering instructions for a dataset — it describes a chart, it does not render an image. Supports scatter, line, bar, histogram, box, heatmap, pie, violin, and correlation plots. Returns a markdown report with the data-point count, the spec, and usage guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of data objects to visualize | |
| title | No | Optional title for the visualization | |
| options | No | Additional visualization options | |
| variables | Yes | Variable names to include in the visualization (properties in data objects) | |
| includeTrendline | No | Include a trendline (for scatter plots) | |
| visualizationType | Yes | Type of visualization to generate |
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.
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.
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.
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.
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.
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.
Rank options against weighted criteria with a weighted-sum decision matrix. Returns a markdown report: ranked options, a per-option breakdown (score × weight contribution, strengths, weaknesses), and a recommendation. Weights are normalized to sum to 1; omit them for equal weighting.
| Name | Required | Description | Default |
|---|---|---|---|
| scores | Yes | Score matrix: one row per option, one score (0-10) per criterion. scores[i][j] rates option i against criterion j. | |
| options | Yes | List of decision options to analyze | |
| weights | No | Optional weights for each criterion (must match criteria length) | |
| criteria | Yes | List of criteria to evaluate options against |
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.
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.
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.
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.
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.
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.
Run a statistical hypothesis test and report the p-value with a reject / fail-to-reject decision at the chosen alpha. Supports independent (Welch) and paired t-tests, Pearson-correlation significance, chi-square independence, and one-way ANOVA. Returns a markdown report with the test statistic, p-value, and conclusion. Use this when you need significance; for descriptive correlation without inference use advanced_statistical_analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Shape depends on testType: t-tests and ANOVA take an array of numeric groups (number[][]); correlation takes two numeric arrays or an array of {x,y} records (see variables); chi_square takes a contingency table (rows x columns of counts). | |
| alpha | No | Significance level for the reject/fail decision, 0.01-0.1 (default 0.05). | |
| testType | Yes | Which test to run: 't_test_independent' (Welch, two independent groups), 't_test_paired' (two paired groups), 'correlation' (Pearson r + significance), 'chi_square' (independence on a contingency table), or 'anova' (one-way, 2+ groups). | |
| variables | No | For 'correlation' only: the two record keys to correlate when data is an array of objects. Ignored otherwise. | |
| alternativeHypothesis | No | Direction: 'less' or 'greater' for a one-sided test; anything else (or omit) is two-sided. |
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.
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.
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.
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.
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.
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.
Assess a natural-language argument for structure, validity, strength, and fallacies. Returns a markdown analysis; 'comprehensive' (default) runs all four plus optional improvement recommendations. Use this for overall argument quality; to only flag and name fallacies use logical_fallacy_detector.
| Name | Required | Description | Default |
|---|---|---|---|
| argument | Yes | The argument to analyze | |
| analysisType | No | Type of analysis to perform | comprehensive |
| includeRecommendations | No | Include recommendations for improving the argument |
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.
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.
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.
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.
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.
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.
Detect and name logical fallacies in text via pattern matching, each with a confidence score, description, and before/after examples. Returns a markdown report grouped by category with an overall severity assessment. Use this to flag specific fallacies; for a full argument assessment use logical_argument_analyzer.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze for logical fallacies | |
| categories | No | Fallacy categories to include: 'informal', 'formal', 'relevance', 'ambiguity', or 'all' (default ['all'] = every category). | |
| includeExamples | No | Include fallacious vs. improved example phrasings (default true). | |
| confidenceThreshold | No | Minimum confidence level to report a fallacy | |
| includeExplanations | No | Include a description of each detected fallacy (default true). |
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.
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.
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.
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.
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.
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.
Score an existing model's predictions against actual values. Classification returns accuracy/precision/recall/F1 from a binary (0/1) confusion matrix; regression returns MSE/MAE/RMSE/R². Returns a markdown report of the requested metrics plus sample count. This scores supplied predictions; to fit a model from raw data use advanced_regression_analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| modelType | Yes | Type of machine learning model | |
| actualValues | Yes | Ground-truth target values; for classification, binary labels encoded as 0 or 1. | |
| predictedValues | Yes | Model predictions, same length/order as actualValues; for classification, 0 or 1. | |
| evaluationMetrics | No | Metrics to report - classification: 'accuracy','precision','recall','f1_score'; regression: 'mse','mae','rmse','r_squared'. Only metrics matching modelType are computed (default ['accuracy','mse']). |
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.
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.
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.
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.
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.
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.
Generate alternative viewpoints on a problem — by stakeholder or discipline — grounded in web research via Exa. Returns a markdown report with key facts and actionable insights per perspective. Requires EXA_API_KEY and ENABLE_RESEARCH_INTEGRATION=true and makes live network calls; it fails without them. To cross-check factual claims instead of generating viewpoints, use verify_research.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | Yes | The problem or situation to examine from new angles. | |
| shiftType | No | How to generate perspectives: 'stakeholder' (default) and 'discipline' are research-backed; 'contrarian', 'optimistic', and 'pessimistic' produce generic framings. | stakeholder |
| includeActionable | No | Include actionable insights with each perspective (default true). | |
| currentPerspective | No | The viewpoint you currently hold, for context (optional). | default |
| numberOfPerspectives | No | How many perspectives to generate, 1-10 (default 3). |
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.
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.
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.
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.
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.
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.
Cross-verify a factual claim across multiple web sources via Exa and return a structured confidence verdict. Returns an object {verifiedResults, confidence:{score, verified, consistencyThreshold, details:{sourceCount, uniqueSources, conflictingClaims, ...}}} — not markdown — from cross-source Jaccard consistency and conflict detection. Requires EXA_API_KEY and ENABLE_RESEARCH_INTEGRATION=true and makes live network calls; it fails without them. To generate alternative viewpoints instead of verifying facts, use perspective_shifter.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The primary factual claim or question to verify. | |
| sources | No | Number of sources to retrieve and cross-verify per query, 1-10 (default 3). | |
| verificationQueries | No | Optional alternate phrasings used to cross-check the claim across additional sources (max 5). | |
| minConsistencyThreshold | No | Cross-source consistency (0-1) required to mark the claim 'verified' (default 0.7). |
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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A Model Context Protocol server for Wix AI tools
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