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StevenWangler

MCP Memory Server

create_relations

Generate and establish multiple new relationships between entities in a knowledge graph using active voice, enabling structured data connections for enhanced information storage and retrieval in the MCP Memory Server.

Instructions

Create multiple new relations between entities in the knowledge graph. Relations should be in active voice

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
relationsYes

Implementation Reference

  • Core implementation of create_relations tool: loads the knowledge graph from file, filters out duplicate relations, appends new relations, saves back to file, and returns the newly created relations.
    async createRelations(relations: Relation[]): Promise<Relation[]> {
      const graph = await this.loadGraph();
      const newRelations = relations.filter(r => !graph.relations.some(existingRelation => 
        existingRelation.from === r.from && 
        existingRelation.to === r.to && 
        existingRelation.relationType === r.relationType
      ));
      graph.relations.push(...newRelations);
      await this.saveGraph(graph);
      return newRelations;
    }
  • src/index.ts:228-249 (registration)
    Registers the 'create_relations' tool with the MCP server by including it in the tools list returned by ListToolsRequestHandler, specifying name, description, and input schema.
    {
      name: "create_relations",
      description: "Create multiple new relations between entities in the knowledge graph. Relations should be in active voice",
      inputSchema: {
        type: "object",
        properties: {
          relations: {
            type: "array",
            items: {
              type: "object",
              properties: {
                from: { type: "string", description: "The name of the entity where the relation starts" },
                to: { type: "string", description: "The name of the entity where the relation ends" },
                relationType: { type: "string", description: "The type of the relation" },
              },
              required: ["from", "to", "relationType"],
            },
          },
        },
        required: ["relations"],
      },
    },
  • Dispatches tool calls to the createRelations handler in the CallToolRequestHandler switch statement.
    case "create_relations":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.createRelations(args.relations as Relation[]), null, 2) }] };
  • TypeScript interface defining the structure of a Relation object used by the create_relations tool.
    interface Relation {
      from: string;
      to: string;
      relationType: string;
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Create multiple new relations', implying a write/mutation operation, but doesn't disclose critical behaviors like whether this requires specific permissions, if relations are immutable after creation, what happens on duplicate relations, or any rate limits. The active voice note is minor and doesn't compensate for the lack of essential operational context.

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—two sentences that directly state the action and a constraint. It's front-loaded with the core purpose, and every word earns its place without redundancy. No extraneous information is included, making it efficient for quick parsing.

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 complexity (a write operation with nested parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what 'entities' are, how relations are validated, what the tool returns, or error conditions. For a mutation tool in a knowledge graph context, this leaves significant gaps for an AI agent to operate safely and effectively.

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

Parameters3/5

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description mentions 'relations' and 'entities' but doesn't explain the parameter structure (e.g., that 'relations' is an array of objects with 'from', 'to', 'relationType'). It adds minimal meaning beyond the bare schema, failing to compensate for the coverage gap, but at least hints at the domain (knowledge graph).

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 verb ('Create') and resource ('multiple new relations between entities in the knowledge graph'), making the purpose understandable. It distinguishes from siblings like 'create_entities' (which creates entities, not relations) and 'delete_relations' (which deletes, not creates). However, it doesn't specify what 'entities' or 'knowledge graph' refer to in this context, leaving some ambiguity.

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 like 'create_entities' or 'add_observations'. It mentions 'relations should be in active voice', which is a stylistic constraint but not a usage guideline. There are no explicit when/when-not instructions or prerequisites, leaving the agent to infer usage from the tool name alone.

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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