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T1nker-1220

Knowledge Graph Memory Server

open_nodes

Retrieve specific entities from a knowledge graph by providing their names to access stored information across chat sessions.

Instructions

Open specific nodes in the knowledge graph by their names

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYesAn array of entity names to retrieve

Implementation Reference

  • The core handler function in KnowledgeGraphManager that loads the full graph, filters entities by the provided names, filters relations to only those connecting the selected entities, and returns the filtered subgraph.
    async openNodes(names: string[]): Promise<KnowledgeGraph> {
      const graph = await this.loadGraph();
    
      // Filter entities
      const filteredEntities = graph.entities.filter(e => names.includes(e.name));
    
      // Create a Set of filtered entity names for quick lookup
      const filteredEntityNames = new Set(filteredEntities.map(e => e.name));
    
      // Filter relations to only include those between filtered entities
      const filteredRelations = graph.relations.filter(r =>
        filteredEntityNames.has(r.from) && filteredEntityNames.has(r.to)
      );
    
      const filteredGraph: KnowledgeGraph = {
        entities: filteredEntities,
        relations: filteredRelations,
      };
    
      return filteredGraph;
  • JSON schema defining the input for the open_nodes tool: an object with a required 'names' array of strings.
    name: "open_nodes",
    description: "Open specific nodes in the knowledge graph by their names",
    inputSchema: {
      type: "object",
      properties: {
        names: {
          type: "array",
          items: { type: "string" },
          description: "An array of entity names to retrieve",
        },
      },
      required: ["names"],
    },
  • index.ts:1242-1243 (registration)
    Registration and dispatch logic in the MCP server's CallToolRequest handler that maps the tool call to the knowledgeGraphManager.openNodes method.
    case "open_nodes":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.openNodes(args.names as string[]), null, 2) }] };
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. It states the action ('open') but doesn't disclose behavioral traits such as what 'open' entails (e.g., retrieving details, expanding nodes, or accessing metadata), whether it's read-only or has side effects, error handling for non-existent nodes, or any rate limits. The description is minimal and lacks critical 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.

Conciseness4/5

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

The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action and resource. However, it could be slightly more informative by including key usage details without sacrificing brevity, keeping it appropriately sized for its simplicity.

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 tool's moderate complexity (involving node retrieval in a knowledge graph), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'open' means operationally, what data is returned, or how it differs from siblings like 'search_nodes'. For a tool with no structured behavioral hints, more context is needed to guide effective use.

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?

The input schema has 100% description coverage, with the 'names' parameter fully documented as 'An array of entity names to retrieve'. The description adds no additional meaning beyond this, such as format examples (e.g., case sensitivity) or constraints (e.g., maximum array size). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 ('open') and resource ('nodes in the knowledge graph'), with specificity about targeting by 'their names'. It distinguishes from siblings like 'search_nodes' (which likely searches rather than opens) and 'read_graph' (which likely reads the entire graph). However, it doesn't explicitly differentiate from 'create_entities' or 'update_lesson_success', which could involve similar resources but different operations.

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 (e.g., nodes must exist), exclusions (e.g., not for creating nodes), or compare to siblings like 'search_nodes' (for finding nodes) or 'read_graph' (for broader access). This leaves 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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