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