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• Clearing outdated observations from research entities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• Removing preliminary analyses that have been superseded by more rigorous methods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