Quantitative Researcher MCP Server

A multi-stage tool for documenting quantitative research sessions, recording statistical analyses, tracking dataset updates, and creating a structured record of research evolution.

When to use this tool:

• Concluding a quantitative research analysis session
• Documenting updates to datasets and variables
• Recording new statistical analyses and test results
• Tracking creation of data visualizations
• Documenting hypothesis test results and conclusions
• Updating statistical model performance information
• Creating a structured record of research activities
• Establishing a formal conclusion to a focused research period
• Building a historical record of project development
• Documenting observations and insights from statistical analysis
• Updating status values for research activities and entities
• Assigning or modifying priority levels for research tasks
• Establishing or modifying sequential relationships between research processes

Key features:

• Provides a structured, multi-stage workflow for research session documentation
• Records dataset updates in the knowledge graph
• Captures new statistical analyses and their results
• Tracks creation of data visualizations and their purposes
• Documents hypothesis test outcomes with statistical significance
• Updates statistical model performance metrics
• Updates project status information
• Maintains session continuity with unique session IDs
• Supports revision of previous stages when needed
• Offers a comprehensive assembly stage that consolidates all session information
• Manages status progression of research activities
• Tracks priority assignments for research tasks
• Documents sequential relationships between research processes

The endsession tool uses a sequential, multi-stage approach with 9 typical stages:

1. Summary Stage: Records basic session information
2. Dataset Updates Stage: Documents changes to datasets
3. New Analyses Stage: Records new statistical tests performed
4. New Visualizations Stage: Documents visualizations created
5. Hypothesis Results Stage: Records outcomes of hypothesis tests
6. Model Updates Stage: Documents changes to statistical models
7. Status Updates Stage: Records changes to entity status values
8. Project Status Stage: Updates the overall project status
9. Assembly Stage: Consolidates all information and finalizes the session record

Parameters explained:

1. sessionId: Required - Unique identifier for the research session

• Obtained from the startsession tool
• Example: "quant_1234567890_abc123"

2. stage: Required - Current stage of the endsession workflow

• Accepts: "summary", "datasetUpdates", "newAnalyses", "newVisualizations", "hypothesisResults", "modelUpdates", "statusUpdates", "projectStatus", or "assembly"
• Each stage has specific data requirements and processing logic

3. stageNumber: Required - The sequence number of the current stage

• Starts at 1 and typically progresses through the stages
• Used to track progress through the session documentation workflow

4. totalStages: Required - Total number of stages planned for this workflow

• Typically 9 for the complete workflow
• Provides context for the progress within the overall process

5. analysis: Optional - Text analysis or observations for the current stage

• Descriptive text explaining the work done in this stage
• Example: "Analyzed multiple regression results and identified significant predictors"

6. stageData: Optional - Stage-specific structured data

• Structure varies by stage type:
  • summary: { summary: "Session summary text", duration: "3 hours", project: "ProjectName" }
  • datasetUpdates: { datasets: [{ name: "Dataset1", size: "500 rows", variables: "10", status: "active", description: "Dataset description" }] }
  • newAnalyses: { analyses: [{ name: "Analysis1", type: "regression", result: "p<0.05", pValue: "0.03", variables: ["var1", "var2"] }] }
  • newVisualizations: { visualizations: [{ name: "Viz1", type: "scatter", description: "Correlation visualization", datasetName: "Dataset1" }] }
  • hypothesisResults: { hypotheses: [{ name: "H1", status: "completed", evidence: "Statistical significance in regression model", pValue: "0.02" }] }
  • modelUpdates: { models: [{ name: "Model1", type: "regression", performance: "R²=0.85", variables: ["var1", "var2"] }] }
  • statusUpdates: { statusUpdates: [{ entityName: "Dataset1", newStatus: "completed", note: "Data cleaning and validation complete" }, { entityName: "Model2", newStatus: "active", note: "Model training in progress" }] }
  • projectStatus: { projectStatus: "active", projectObservation: "Data analysis phase complete", priorityUpdates: [{ entityName: "AnalysisTask1", priority: "high", note: "Critical for upcoming publication" }], sequenceUpdates: [{ before: "DataCleaning", after: "ModelTraining", note: "Reorganized analysis workflow" }] }
  • assembly: No stageData needed - automatically assembled from previous stages

7. nextStageNeeded: Required - Whether additional stages are needed after this one

• Boolean value (true/false)
• Set to false on the final stage to complete the session

8. isRevision: Optional - Whether this is revising a previous stage

• Boolean value (true/false)
• Default: false

9. revisesStage: Optional - If revising, which stage number is being revised

• Required when isRevision is true
• Indicates which previous stage is being updated

Status and Priority Management:

• The statusUpdates stage allows for batch updates to entity status values
• Valid status values include: active, completed, pending, abandoned
• Priority assignments (high, low) can be modified in the projectStatus stage
• Status changes are implemented through has_status relations
• Priority changes are implemented through has_priority relations
• Status and priority changes are tracked to maintain research progress history

Sequential Process Management:

• The projectStatus stage allows for defining or modifying sequential relationships
• The precedes relation is used to establish logical ordering between research processes
• Sequential updates help maintain a coherent research workflow
• Process sequences can be visualized through the loadcontext tool
• Critical research sequences are maintained to ensure methodological integrity

When the endsession workflow completes (assembly stage with nextStageNeeded: false), the tool performs these updates:

1. Dataset Entities: Updates existing datasets or creates new dataset entities with the provided information
2. Statistical Analyses: Creates entities for statistical tests and links them to projects and variables
3. Visualizations: Creates entities for data visualizations and links them to datasets and projects
4. Hypothesis Updates: Updates existing hypotheses or creates new hypothesis entities with test results
5. Model Updates: Updates existing model entities or creates new models with performance metrics
6. Status Updates: Updates entity status values through has_status relations
7. Priority Updates: Updates entity priority values through has_priority relations
8. Sequence Updates: Updates sequential relationships through precedes relations
9. Project Status: Updates the project status, adds an updated timestamp, and records observations

Return information:

• JSON response with the following structure when stages are in progress:
  • success: Boolean indicating whether the operation succeeded
  • stageCompleted: The stage that was just completed
  • nextStageNeeded: Whether more stages are required
  • stageResult: The processed result of the current stage
• Formatted markdown text summary when the session is completed, including:
  • Session date and project name
  • Summary of the session
  • Dataset updates
  • New statistical analyses
  • New visualizations
  • Hypothesis test results
  • Model updates
  • Status changes
  • Priority modifications
  • Sequential relationship updates
  • Project status update

You should:

• Complete all stages in order for comprehensive session documentation
• Provide specific details in each stage for accurate research documentation
• Specify dataset updates with clear size, variable count, and status information
• Include p-values and variable names for statistical analyses
• Connect visualizations to specific datasets when possible
• Document hypothesis test results with evidence and significance levels
• Include performance metrics when updating statistical models
• Update entity status using has_status relations with valid status values (active, completed, pending, abandoned)
• Assign priorities using has_priority relations with valid priority values (high, low)
• Define process sequences using precedes relations to establish research workflows
• Include relevant observations for project status updates
• If making a revision, specify which stage is being revised
• Only mark nextStageNeeded as false on the final assembly stage
• Review the final summary message to confirm all session details were recorded properly
• Use the unique session ID consistently across all stages