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• Clearing outdated observations from research entities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• Removing preliminary analyses that have been superseded by more rigorous methods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