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T1nker-1220

Knowledge Graph Memory Server

by T1nker-1220

create_entities

Add multiple new entities to a knowledge graph by specifying names, types, and associated observations for persistent memory storage.

Instructions

Create multiple new entities in the knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entitiesYes

Implementation Reference

  • Core handler function in KnowledgeGraphManager that creates new entities: loads graph, filters duplicates by name, appends new entities, saves graph, returns created entities.
    async createEntities(entities: Entity[]): Promise<Entity[]> {
      const graph = await this.loadGraph();
      const newEntities = entities.filter(e => !graph.entities.some(existingEntity => existingEntity.name === e.name));
      graph.entities.push(...newEntities);
      await this.saveGraph(graph);
      return newEntities;
  • Input schema definition for the create_entities tool, specifying structure for array of entities with required fields: name, entityType, observations.
    inputSchema: {
      type: "object",
      properties: {
        entities: {
          type: "array",
          items: {
            type: "object",
            properties: {
              name: { type: "string", description: "The name of the entity" },
              entityType: { type: "string", description: "The type of the entity" },
              observations: {
                type: "array",
                items: { type: "string" },
                description: "An array of observation contents associated with the entity"
              },
            },
            required: ["name", "entityType", "observations"],
          },
        },
      },
      required: ["entities"],
    },
  • TypeScript interface defining the Entity structure used by create_entities.
    interface Entity {
      name: string;
      entityType: string;
      observations: string[];
      metadata?: Metadata;  // Making metadata optional for backward compatibility
    }
  • index.ts:926-951 (registration)
    Tool registration in the ListTools response, defining name, description, and input schema.
    {
      name: "create_entities",
      description: "Create multiple new entities in the knowledge graph",
      inputSchema: {
        type: "object",
        properties: {
          entities: {
            type: "array",
            items: {
              type: "object",
              properties: {
                name: { type: "string", description: "The name of the entity" },
                entityType: { type: "string", description: "The type of the entity" },
                observations: {
                  type: "array",
                  items: { type: "string" },
                  description: "An array of observation contents associated with the entity"
                },
              },
              required: ["name", "entityType", "observations"],
            },
          },
        },
        required: ["entities"],
      },
    },
  • Dispatcher case in CallToolRequestSchema handler that invokes the createEntities method and formats JSON response.
    case "create_entities":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.createEntities(args.entities as Entity[]), null, 2) }] };

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it creates entities, but does not mention potential duplicate handling, overwrite behavior, validation rules, or whether the operation is atomic—information an agent would need for a mutating tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that communicates the core purpose with no filler. It is appropriately concise for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of annotations, an output schema, and limited schema coverage, the description should offer more context about usage, side effects, or return behavior. It does not, leaving significant gaps for an agent to make assumptions about how the tool behaves.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema's top-level 'entities' parameter has no description (coverage 0%), and the tool description does not explain what constitutes an entity (name, type, observations). The description adds no value beyond the bare phrase 'multiple new entities,' failing to compensate for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Create') and a specific resource ('multiple new entities in the knowledge graph'), clearly distinguishing this from sibling tools like create_relations and add_observations. It is explicit and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention create_relations, add_observations, or any exclusions, leaving the agent to infer selection based solely on the name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.