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