Quantitative Researcher MCP Server

A sophisticated query tool for exploring, analyzing, and retrieving complex information from the quantitative research knowledge graph. When to use this tool: - Retrieving a comprehensive view of your entire research knowledge structure - Searching for specific research entities across your quantitative data projects - Getting detailed information about particular research projects, datasets, or statistical elements - Exploring relationships between variables and their statistical properties - Analyzing hypothesis test results and their implications - Retrieving statistical model performance metrics - Accessing visualization galleries for specific projects or datasets - Examining variable distributions and their statistical properties - Finding connections between different aspects of your research - Creating statistical reports or summaries from your data - Exploring the relationships between research questions and findings - Identifying entities by status to track research progress - Filtering tasks by priority to manage research workflow - Analyzing sequential relationships between research processes Key features: - Offers specialized operations for querying different aspects of quantitative research data - Retrieves complete or filtered views of the research knowledge graph - Provides flexible search capabilities across all research entities - Supports detailed exploration of specific entities by name - Generates specialized views for projects, datasets, hypotheses, and variables - Retrieves statistical results, visualizations, and model performance metrics - Provides detailed variable distribution analysis - Identifies related entities to explore connections within your research - Returns consistently structured JSON responses for easy processing - Facilitates depth and breadth exploration of quantitative data - Supports status-based filtering of research entities - Enables priority-based task management - Provides sequential process analysis capabilities Parameters explained: 1. type: The type of query operation to perform - Accepts one of the specialized operations: "graph", "search", "nodes", "project", "dataset", "hypothesis", "variables", "statistics", "visualizations", "model", "question", "distribution", "related", "status", "priority", "sequence" - Determines how the params parameter is interpreted 2. params: Operation-specific parameters (structure varies by type): - For "graph": No parameters needed (retrieves the full research knowledge graph) - For "search": Object containing: * query: Search string to find entities (supports entity type filters) - For "nodes": Object containing: * names: Array of entity names to retrieve - For "project": Object containing: * projectName: Name of the project to retrieve details for - For "dataset": Object containing: * datasetName: Name of the dataset to retrieve analysis for - For "hypothesis": Object containing: * projectName: Project name to filter hypotheses by * hypothesisName: (Optional) Specific hypothesis to retrieve tests for - For "variables": Object containing: * variableName: Name of the variable to retrieve relationship information for - For "statistics": Object containing: * projectName: Project name to retrieve statistical results for * testType: (Optional) Type of statistical test to filter by - For "visualizations": Object containing: * projectName: Project name to retrieve visualizations for * datasetName: (Optional) Dataset name to filter visualizations by - For "model": Object containing: * modelName: Name of the model to retrieve performance metrics for - For "question": Object containing: * questionName: Name of the research question to retrieve results for - For "distribution": Object containing: * variableName: Name of the variable to analyze distribution of * datasetName: (Optional) Dataset name to contextualize the variable - For "related": Object containing: * entityName: Name of the entity to find related entities for - For "status": Object containing: * statusValue: The status value to filter by (e.g., "active", "completed", "pending", "abandoned") - For "priority": Object containing: * priorityValue: The priority value to filter by (e.g., "high", "low") - For "sequence": Object containing: * entityName: Name of the entity to find sequential relationships for Operation details: - graph: Returns the complete research knowledge graph with all entities and relationships - search: Performs text-based search across entity names and observations - nodes: Retrieves detailed information about specific entities by name - project: Returns comprehensive project information including datasets, hypotheses, tests, and findings - dataset: Provides detailed dataset analysis with variables, descriptive statistics, and correlations - hypothesis: Retrieves hypothesis tests and their results for a project or specific hypothesis - variables: Examines relationships between a variable and other variables (correlations, dependencies) - statistics: Collects statistical test results for a project, optionally filtered by test type - visualizations: Returns visualization metadata and descriptions for a project or dataset - model: Provides detailed model performance metrics, parameters, and validation results - question: Retrieves research question details, related hypotheses, and supporting findings - distribution: Analyzes the statistical distribution of a variable with descriptive stats and normality tests - related: Identifies all entities directly connected to a specific entity - status: Retrieves all entities with a specific status value - priority: Retrieves all entities with a specific priority value - sequence: Identifies sequential relationships for a specific entity showing preceding and following entities Status and Priority Information: - Status queries return entities organized by their current research stage - Priority queries help identify critical research tasks and elements - Status values include: active, completed, pending, abandoned - Priority values include: high, low - Status and priority are assigned through has_status and has_priority relations Sequential Process Information: - Sequence queries identify entities that come before or after in a research process - Sequential relationships help visualize the research workflow and methodology - The sequence operation shows both incoming and outgoing precedes relations - Process sequences are essential for understanding multi-step analytical procedures Return information: - success: Boolean indicating whether the operation succeeded - Additional fields depend on the operation type: * graph: Complete knowledge graph * results: For search operations * nodes: For specific entity retrieval * project/dataset/hypothesis/etc.: For specialized views * status/priority: Lists of entities with specified status/priority values * sequence: Preceding and following entities in research processes You should: - Start with broad queries ("graph", "search") to explore your research corpus - Use specific entity queries ("nodes", "project", "dataset") for detailed information - Examine variable relationships and distributions to understand your data - Review hypothesis tests and statistical results to evaluate evidence - Explore model performance metrics to assess predictive accuracy - Use visualization galleries to communicate research findings - Examine research questions and their supporting evidence - Use status queries to identify all entities at a particular research stage - Use priority queries to focus on high-priority research tasks - Use sequence queries to understand process flows in your research methodology - Combine multiple operations to build a comprehensive understanding of your research - Use the related operation to discover connections between entities - Apply search filters to find specific types of research elements