Quantitative Researcher MCP Server

A versatile tool for constructing and enhancing the quantitative research knowledge graph by adding new research elements, relationships, and observations. When to use this tool: - Creating new research entities (projects, datasets, variables, hypotheses, statistical tests, etc.) - Establishing relationships between research elements (e.g., connecting variables to datasets, statistical tests to hypotheses) - Adding observations, properties, or metadata to existing research entities - Building the research corpus incrementally as data collection and analysis progress - Organizing and structuring quantitative data within your research framework - Documenting statistical analyses, models, and their results - Tracking research questions and linking them to findings - Creating visualizations and connecting them to data and analyses - Setting status values for research activities and entities - Assigning priorities to research tasks and activities - Defining sequential relationships between research processes Key features: - Creates three distinct types of knowledge graph elements: entities, relations, and observations - Supports specialized quantitative research entity types (projects, datasets, variables, hypotheses, statistical tests, etc.) - Validates entity and relation types against predefined standards for the quantitative research domain - Handles batch creation of multiple entities or relations in a single operation - Returns confirmation with details of created elements - Ensures proper data typing and structure for the quantitative research knowledge graph - Enables comprehensive documentation of statistical analysis processes - Supports status and priority assignment through entity-relation model - Enables sequential relationships through precedes relation Parameters explained: 1. type: The type of creation operation to perform - Accepts: "entities", "relations", or "observations" - Determines how the data parameter is interpreted 2. data: The content to add to the knowledge graph (structure varies by type): - For "entities": An array of objects, each containing: * name: Unique identifier for the entity * entityType: One of the valid entity types (project, dataset, variable, hypothesis, statisticalTest, result, analysisScript, visualization, model, literature, researchQuestion, finding, participant, status, priority) * observations: Array of strings containing notes or properties about the entity - For "relations": An array of objects, each containing: * from: Name of the source entity * to: Name of the target entity * relationType: The type of relationship between entities (e.g., correlates_with, predicts, tests, analyzes, produces, has_status, has_priority, precedes) - For "observations": Either a single object or an array of objects, each containing: * entityName: Name of the entity to add observations to * contents: Array of strings with new observations to add Valid entity types: - project: Overall research study - dataset: Collection of data used for analysis - variable: Specific measurable attribute in a dataset - hypothesis: Formal testable statement - statisticalTest: Analysis method applied to data - result: Outcome of statistical analysis - analysisScript: Code used to perform analysis - visualization: Visual representation of data - model: Statistical/mathematical model - literature: Academic sources - researchQuestion: Formal questions guiding the study - finding: Results or conclusions - participant: Research subjects - status: Entity status values - priority: Entity priority values Valid relation types: - correlates_with: Statistical correlation between variables - predicts: Predictive relationship from independent to dependent variable - tests: Statistical test examines hypothesis - analyzes: Analysis performed on dataset - produces: Analysis produces result - visualizes: Visualization displays data or result - contains: Hierarchical relationship - part_of: Entity is part of another entity - depends_on: Dependency relationship - supports: Evidence supporting a hypothesis or finding - contradicts: Evidence contradicting a hypothesis or finding - derived_from: Entity is derived from another entity - controls_for: Variable/method controls for confounds - moderates: Variable moderates a relationship - mediates: Variable mediates a relationship - implements: Script implements statistical test/model - compares: Statistical comparison between groups/variables - includes: Model includes variables - validates: Validates a model or result - cites: References literature - has_status: Links entity to its status - has_priority: Links entity to its priority - precedes: Entity comes before another entity in sequence Status information: - Valid status values include: active, completed, pending, abandoned - Status is assigned through the has_status relation type - Status helps track progress of research activities Priority information: - Valid priority values: high, low - Priority is assigned through the has_priority relation type - Priority helps identify critical research tasks Sequential Process Information: - The precedes relation establishes logical ordering between research processes - Sequential relationships document the flow of the research methodology - Helps maintain proper order in multi-step analysis procedures Return information: - JSON response indicating success or failure - For successful operations: * Success flag set to true * Details of created elements in the "created" field (for entities/relations) or "added" field (for observations) - For failed operations: * Success flag set to false * Error message describing the issue Error handling: - Validates entity types against the predefined list for quantitative research - Validates relation types against acceptable standards - Returns descriptive error messages for invalid inputs - Gracefully handles type mismatches and formatting errors You should: - Use consistent naming conventions for entities to facilitate relationships and retrieval - Begin by creating projects and datasets before more specific research elements - Add detailed observations to entities to enhance context and retrievability - Create relationships to build a comprehensive network of interconnected research data - Document statistical methodology thoroughly by connecting tests, variables, and hypotheses - Add statistical results with appropriate metadata (p-values, effect sizes, confidence intervals) - Create visualizations and link them to the data they represent - Use relations to document the flow of analysis from data to findings - Connect literature to support hypotheses and contextualize findings - Structure models with clear relationships to the variables they include - Document analysis scripts with information about their purpose and implementation - Use has_status relations to track the progress of research activities (active, completed, pending, abandoned) - Use has_priority relations to indicate important research elements (high, low) - Use precedes relations to establish sequences in research methodologies