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StevenWangler

MCP Memory Server

search_nodes

Query nodes in the MCP Memory Server's knowledge graph to match entity names, types, and observation content, enabling precise information retrieval for LLMs.

Instructions

Search for nodes in the knowledge graph based on a query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query to match against entity names, types, and observation content

Implementation Reference

  • Implementation of the searchNodes method in KnowledgeGraphManager class, which filters entities matching the query in name, type, or observations, and includes relations between matching entities.
    async searchNodes(query: string): Promise<KnowledgeGraph> {
      const graph = await this.loadGraph();
      
      // Filter entities
      const filteredEntities = graph.entities.filter(e => 
        e.name.toLowerCase().includes(query.toLowerCase()) ||
        e.entityType.toLowerCase().includes(query.toLowerCase()) ||
        e.observations.some(o => o.toLowerCase().includes(query.toLowerCase()))
      );
    
      // 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;
    }
  • Input schema definition for the search_nodes tool, specifying a required 'query' string parameter.
    inputSchema: {
      type: "object",
      properties: {
        query: { type: "string", description: "The search query to match against entity names, types, and observation content" },
      },
      required: ["query"],
    },
  • src/index.ts:347-356 (registration)
    Registration of the search_nodes tool in the listTools response, including name, description, and input schema.
      name: "search_nodes",
      description: "Search for nodes in the knowledge graph based on a query",
      inputSchema: {
        type: "object",
        properties: {
          query: { type: "string", description: "The search query to match against entity names, types, and observation content" },
        },
        required: ["query"],
      },
    },
  • Dispatcher case in the CallToolRequestSchema handler that invokes the searchNodes method with the provided query argument.
    case "search_nodes":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.searchNodes(args.query 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 of behavioral disclosure. It states the tool searches nodes but doesn't describe behavioral traits such as search scope (e.g., partial vs. exact matches), performance characteristics (e.g., speed, limitations), or output format (e.g., list of nodes, pagination). This is a significant gap for a search tool with no annotation coverage.

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, clear sentence with no wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, making it efficient for an agent to parse.

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 of a search operation, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns (e.g., node details, IDs), how results are structured, or any limitations (e.g., result count, sorting). For a tool with no structured output information, 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 'query' parameter documented as matching 'entity names, types, and observation content.' The description adds no additional parameter semantics beyond this, as it only repeats 'based on a query.' With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.

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 tool's purpose: 'Search for nodes in the knowledge graph based on a query.' It specifies the verb ('search'), resource ('nodes in the knowledge graph'), and mechanism ('based on a query'). However, it doesn't explicitly differentiate from sibling tools like 'open_nodes' or 'read_graph,' which might also retrieve node information.

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 sibling tools like 'open_nodes' or 'read_graph,' nor does it specify use cases, prerequisites, or exclusions. This leaves the agent without context for tool selection.

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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