Quantitative Researcher MCP Server

An MCP server implementation that provides tools for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results. This server helps quantitative researchers organize their data, track their analyses, evaluate hypotheses, and generate insights from numerical data.

Features

Persistent Research Context: Maintain a structured knowledge graph of research entities and relationships across multiple analysis sessions
Study Session Management: Track research analysis sessions with unique IDs and record progress over time
Hypothesis Testing: Track hypotheses, their associated tests, and resulting conclusions
Dataset Management: Organize and track descriptive statistics and variables within datasets
Statistical Analysis: Record statistical tests, models, and their results
Variable Relationships: Track correlations, predictions, and other relationships between variables
Research Question Tracking: Link data analyses to specific research questions
Data Visualization: Document visualizations created from datasets and results
Model Performance: Monitor statistical model performance metrics
Research Finding Documentation: Link findings to supporting statistical evidence
Research Methodology Documentation: Track methodological decisions and approaches

Entities

The Quantitative Researcher MCP Server recognizes the following 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 (active, completed, pending, abandoned)
priority: Priority level values (high, low)

Relationships

Entities can be connected through the following relationship 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 entities to their current status (active, completed, pending, abandoned)
has_priority: Links entities to their priority level (high, low)
precedes: Indicates that one process or activity comes before another in a sequence

Available Tools

The Quantitative Researcher MCP Server provides these tools for interacting with research knowledge:

startsession

Starts a new quantitative research session, generating a unique session ID and displaying current research projects, datasets, models, visualizations, and previous sessions. Shows status information via has_status relations, priority levels via has_priority relations, and identifies activities ready to be worked on next based on sequential process relationships.

loadcontext

Loads detailed context for a specific entity (project, dataset, variable, etc.), displaying relevant information based on entity type. Includes status information, priority levels, and sequential process relationships.

endsession

Records the results of a research session through a structured, multi-stage process:

summary: Records session summary, duration, and project focus
datasetUpdates: Documents updates to datasets during the session
newAnalyses: Records new statistical analyses performed
newVisualizations: Tracks new data visualizations created
hypothesisResults: Documents results of hypothesis testing
modelUpdates: Records updates to statistical models
statusUpdates: Records changes to entity status values
projectStatus: Updates overall project status, priority assignments, and sequential relationships
assembly: Final assembly of all session data

buildcontext

Creates new entities, relations, or observations in the knowledge graph:

entities: Add new research entities (projects, datasets, variables, status, priority, etc.)
relations: Create relationships between entities (including has_status, has_priority, precedes)
observations: Add observations to existing entities

deletecontext

Removes entities, relations, or observations from the knowledge graph:

entities: Remove research entities
relations: Remove relationships between entities (including status, priority, and sequential relations)
observations: Remove specific observations from entities

advancedcontext

Retrieves information from the knowledge graph:

graph: Get the entire knowledge graph
search: Search for nodes based on query criteria
nodes: Get specific nodes by name
related: Find related entities
status: Find entities with a specific status value (active, completed, pending, abandoned)
priority: Find entities with a specific priority value (high, low)
sequence: Identify sequential relationships for research processes

Domain-Specific Functions

The Quantitative Researcher MCP Server includes specialized domain functions for quantitative research:

getProjectOverview: Comprehensive view of a project including research questions, methodology, datasets, variables
getDatasetAnalysis: Analysis of dataset contents including variables, descriptive statistics, and data quality
getHypothesisTests: Review of hypothesis tests and their outcomes
getVariableRelationships: Examine correlations, predictions, and other relationships between variables
getStatisticalResults: Summarize the results of statistical analyses
getVisualizationGallery: View visualizations created for datasets and results
getModelPerformance: Assess performance metrics for statistical models
getResearchQuestionResults: Organize analyses and results by research questions
getVariableDistribution: Examine the distribution and properties of individual variables
getStatusOverview: View all entities with a specific status (active, completed, pending, abandoned)
getPriorityItems: Identify high-priority research tasks and activities
getResearchSequence: Visualize the sequence of research processes based on precedes relations

Example Prompts

Starting a Session

Let's start a new quantitative research session for my Climate Impact Study project.

Loading Research Context

Load the context for the Climate Impact Study project so I can see the current state of my statistical analyses.

Recording Session Results

I've just finished analyzing data for my Climate Impact Study. I ran three new regression models to test the relationship between temperature and crop yield, created two visualizations of the correlation patterns, and confirmed our hypothesis about rainfall effects. I've marked the temperature analysis as complete and assigned high priority to the regional variation analysis. The model performance improved by 15% after controlling for regional variations.

Managing Research Knowledge

Create a new variable called "Annual Precipitation" that's part of the "Climate Measures" dataset with observations noting it's normally distributed with a mean of 34.5 inches. Set its status to active and make it precede the "Crop Yield Analysis" process.

Update the status of the "Data Cleaning" process to "completed" and add an observation that all outliers have been properly handled.

Usage

This MCP server enables quantitative researchers to:

Maintain Analytical Continuity: Track analyses and results across multiple research sessions
Organize Statistical Evidence: Link hypotheses to supporting statistical tests and results
Document Variable Relationships: Record how variables correlate, predict, or influence each other
Track Model Development: Document the evolution of statistical models and their performance
Support Result Interpretation: Connect statistical findings to research questions and theoretical frameworks
Ensure Methodological Rigor: Document methodological decisions and analytical approaches
Prepare Research Reports: Organize statistical evidence to support research findings
Track Research Progress: Monitor entity status throughout the research lifecycle
Prioritize Research Tasks: Identify and focus on high-priority research activities
Sequence Research Processes: Plan and visualize the logical order of research and analytical steps

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Install from GitHub and run with npx

{
  "mcpServers": {
    "quantitativeresearch": {
      "command": "npx",
      "args": [
        "-y",
        "github:tejpalvirk/quantitativeresearch"
      ]
    }
  }
}

Install globally and run directly

First, install the package globally:

npm install -g github:tejpalvirk/quantitativeresearch

Then configure Claude Desktop:

{
  "mcpServers": {
    "quantitativeresearch": {
      "command": "contextmanager-quantitativeresearch"
    }
  }
}

docker

{
  "mcpServers": {
    "quantitativeresearch": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "mcp/quantitativeresearch"
      ]
    }
  }
}

Building

From Source

# Clone the repository
git clone https://github.com/tejpalvirk/contextmanager.git
cd contextmanager

# Install dependencies
npm install

# Build the server
npm run build

# Run the server
cd quantitativeresearch
node quantitativeresearch_index.js

Docker:

docker build -t mcp/quantitativeresearch -f quantitativeresearch/Dockerfile .

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Environment Variables

The Quantitative Research MCP Server supports the following environment variables to customize where data is stored:

MEMORY_FILE_PATH: Path where the knowledge graph data will be stored
- Can be absolute or relative (relative paths use current working directory)
- Default: ./quantitativeresearch/memory.json
SESSIONS_FILE_PATH: Path where session data will be stored
- Can be absolute or relative (relative paths use current working directory)
- Default: ./quantitativeresearch/sessions.json

Example usage:

# Store data in the current directory
MEMORY_FILE_PATH="./quantitative-memory.json" SESSIONS_FILE_PATH="./quantitative-sessions.json" npx github:tejpalvirk/contextmanager-quantitativeresearch

# Store data in a specific location (absolute path)
MEMORY_FILE_PATH="/path/to/data/quantitative-memory.json" npx github:tejpalvirk/contextmanager-quantitativeresearch

# Store data in user's home directory
MEMORY_FILE_PATH="$HOME/contextmanager/quantitative-memory.json" npx github:tejpalvirk/contextmanager-quantitativeresearch