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