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