Quantitative Researcher MCP Server

A powerful tool for retrieving detailed contextual information about quantitative research entities, providing rich statistical insights tailored to each entity type.

When to use this tool:

• Retrieving comprehensive information about research projects, datasets, variables, and statistical elements
• Exploring the statistical relationships between variables
• Examining hypothesis tests and their results
• Reviewing model performance metrics and parameters
• Analyzing dataset properties and descriptive statistics
• Inspecting visualizations related to specific datasets or projects
• Understanding statistical test results and their significance
• Preparing for statistical analysis by establishing data context
• Examining variable distributions and correlations
• Getting a holistic view of quantitative research progress
• Tracking research activities by their current status
• Managing tasks based on their assigned priorities
• Understanding sequential relationships between research processes

Key features:

• Provides richly formatted, context-aware information about quantitative research entities
• Adapts output format based on entity type (project, dataset, variable, model, hypothesis, statistical_test)
• Presents both direct entity information and related statistical elements
• Shows statistical metrics, p-values, and significance levels
• Tracks entity views within the current research session
• Formats information in a structured, readable markdown format
• Highlights relationships between variables and statistical tests
• Presents performance metrics for statistical models
• Shows dataset characteristics and variable properties
• Includes status information for tracking research progress
• Displays priority assignments for critical research elements
• Visualizes sequential relationships between research processes

Parameters explained:

1. entityName: Required - The name of the entity to retrieve context for

• Example: "Customer Satisfaction Study", "Survey_Dataset", "Age_Variable"

2. entityType: Optional - The type of entity being retrieved

• Default: "project"
• Helps the system format the output appropriately
• Common types include: "project", "dataset", "variable", "model", "hypothesis", "statistical_test", "status", "priority"

3. sessionId: Optional - The current session identifier

• Typically provided by startsession
• Used for tracking entity views within the session

Each entity type returns specialized context information:

• Project: Shows project status (via has_status), description, datasets, hypotheses, statistical tests, models, key visualizations, and priority (via has_priority)
• Dataset: Displays project affiliation, status (via has_status), size, variable count, descriptive statistics, visualizations, and models trained on it
• Variable: Shows data type, role, scale, descriptive statistics, normality tests, and correlations with other variables
• Model: Displays type, training dataset, creation date, status (via has_status), performance metrics, and model parameters
• Hypothesis: Shows status (via has_status), p-value, creation date, associated tests, and project affiliation
• Statistical Test: Shows test type, result, p-value, date, variables analyzed, and hypotheses tested
• Status: Shows all entities assigned this status value, organized by entity type
• Priority: Shows all entities assigned this priority value, organized by entity type
• Other Entity Types: Shows basic entity information and observations

Status and Priority Information:

• All entity displays include status information when available via has_status relations
• Priority assignments are shown for research tasks and other prioritized elements
• Valid status values include: active, completed, pending, abandoned
• Valid priority values include: high, low

Sequential Process Relationships:

• Entity displays show preceding and following entities through precedes relations
• Process sequences are visualized to show workflow between research activities
• Research phases and activities display their position in the overall analytical pipeline
• Sequential relationships help understand dependencies in multi-step analysis processes

Return information:

• Formatted markdown text with hierarchical structure
• Sections adapted to the specific entity type
• Related entities shown with their statistical properties
• Status and priority information prominently displayed
• Sequential relationships clearly indicated
• Error messages if the entity doesn't exist or can't be retrieved

You should:

• Specify the exact entity name for accurate retrieval
• Provide the entity type when possible for optimally formatted results
• Start with project entities to get a high-level overview of research
• Examine dataset context to understand variable relationships
• Review variable context to understand distributions and correlations
• Use hypothesis context to assess research question outcomes
• Explore model context to evaluate predictive performance
• Examine statistical test context to understand analysis results
• Check status entities to see all research elements at the same stage
• Review priority entities to identify critical research tasks
• Explore sequential relationships to understand analysis workflows
• After retrieving context, follow up on specific entities of interest
• Use in conjunction with startsession to maintain session tracking
• Remember that this tool only retrieves existing information; use buildcontext to add new entities