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