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T1nker-1220

Knowledge Graph Memory Server

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) }] };
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a creation operation but doesn't mention permissions needed, whether entities are permanent or reversible, rate limits, or what happens on partial failure when creating multiple entities. For a mutation tool with zero annotation coverage, this is inadequate.

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 extremely concise at just 7 words, front-loading the essential information with zero wasted words. Every word earns its place in communicating the core functionality.

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?

For a mutation tool that creates multiple entities with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what happens after creation, error conditions, or provide enough context for safe and effective use despite the tool's apparent complexity.

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?

Schema description coverage is 0%, so the description must compensate but doesn't. It mentions 'entities' but provides no information about what constitutes an entity, what 'entityType' values are valid, or what 'observations' should contain. The single parameter 'entities' array remains largely unexplained beyond its name.

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

Purpose4/5

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

The description clearly states the action ('Create') and resource ('multiple new entities in the knowledge graph'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'create_lesson' or 'create_relations', which also create things in the knowledge graph system, so it doesn't reach the highest score.

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 doesn't mention prerequisites, when not to use it, or how it differs from sibling tools like 'create_lesson' or 'add_observations', leaving the agent to infer usage context.

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

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