Quantitative Researcher MCP Server

A precise tool for removing elements from the quantitative research knowledge graph, enabling researchers to maintain data accuracy and refine their analytical framework. When to use this tool: - Removing incorrect or duplicate research entities - Deleting erroneous relationships between research elements - Clearing outdated observations from research entities - Restructuring your research framework as analysis evolves - Removing invalid statistical tests or models - Correcting relationships between variables, datasets, or results - Cleaning up the knowledge graph during research refinement phases - Eliminating deprecated hypotheses or findings that are no longer supported - Removing preliminary analyses that have been superseded by more rigorous methods - Reorganizing your analytical structure by removing and recreating elements - Updating status assignments when research activities change state - Modifying priority assignments as research focus shifts - Restructuring sequential relationships between research processes Key features: - Provides targeted deletion capabilities for three distinct types of knowledge graph elements: entities, relations, and observations - Maintains knowledge graph integrity during deletion operations - Supports batch deletion of multiple items in a single operation - Returns clear confirmation of deletion results - Preserves the overall structure of the research knowledge graph while removing specific elements - Performs validation to ensure deletion requests are properly formatted - Handles status and priority relation management - Supports modification of sequential process relationships Parameters explained: 1. type: The type of deletion operation to perform - Accepts: "entities", "relations", or "observations" - Determines how the data parameter is interpreted 2. data: The elements to remove from the knowledge graph (structure varies by type): - For "entities": Array of entity names to delete * Example: ["Dataset_2021", "Hypothesis_A", "Model_Linear", "Status_Completed"] - For "relations": Array of relation objects, each containing: * from: Name of the source entity * to: Name of the target entity * relationType: Type of relationship to remove (e.g., "correlates_with", "has_status", "has_priority", "precedes") * Example: [{ "from": "Variable_Age", "to": "Variable_Income", "relationType": "correlates_with" }] - For "observations": Array of objects, each containing: * entityName: Name of the entity to remove observations from * observations: Array of specific observations to remove * Example: [{ "entityName": "Dataset_Main", "observations": ["size:1000", "collection_date:2022-05-15"] }] Deletion behavior by type: - Entities: Removes the specified entities and all their associated relations from the knowledge graph - Relations: Removes only the specified relationships, leaving the connected entities intact - Observations: Removes specific observations from entities while preserving the entities themselves Status and Priority Management: - When deleting status or priority entities, be aware of the impact on entities that reference them - For changing an entity's status, delete the existing has_status relation before creating a new one - For changing priority, delete the existing has_priority relation before creating a new one - Status values (active, completed, pending, abandoned) are managed through relations, not direct properties - Priority values (high, low) are managed through relations, not direct properties Sequential Process Management: - Removing precedes relations affects the logical flow of research processes - When reorganizing research phases, update all affected precedes relations - Consider restructuring sequential relationships after deletion to maintain methodological continuity - Sequential relationships are important for maintaining proper order in multi-step analyses Safety considerations: - Entity deletion is permanent and will also remove all relationships involving those entities - Consider exporting or backing up your research knowledge graph before performing large-scale deletions - For sensitive operations, consider removing specific observations rather than entire entities - When removing statistical tests or results, consider the impact on your overall analysis framework - Status changes should be carefully managed to maintain accurate research progress tracking - Changes to sequential relationships may affect dependent research activities Return information: - JSON response indicating success or failure - For successful operations: - Success flag set to true - Confirmation message with count of deleted items * For entities: "Deleted X entities" * For relations: "Deleted X relations" * For observations: "Deleted observations from X entities" - For failed operations: - Success flag set to false - Error message describing the issue You should: - Be specific in your deletion requests to avoid unintended data loss - Use relations deletion when you want to disconnect entities without removing them - For observations, provide the exact observations to ensure only the intended content is removed - When restructuring your analysis, consider how deletions will affect related elements - Use deletecontext in conjunction with buildcontext to refine and evolve your research framework - Regularly review your knowledge graph for elements that may need to be removed or updated - Consider the cascading effects of entity deletion on your overall research structure - Delete outdated statistical results when new analyses are performed - Remove incorrect relationships between variables when better understanding is gained - When updating entity status, delete the old has_status relation before creating a new one - When updating entity priority, delete the old has_priority relation before creating a new one - Maintain logical consistency when modifying sequential analysis relationships