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