Skip to main content
Glama
T1nker-1220

Knowledge Graph Memory Server

search_nodes

Find nodes in a knowledge graph by searching entity names, types, and observation content with a query to retrieve relevant information.

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

  • Core handler function implementing the search logic: loads graph, filters entities by query match in name/type/observations, filters connected relations, caches result.
    async searchNodes(query: string): Promise<KnowledgeGraph> {
      const cacheKey = this.getCacheKey('searchNodes', { query });
      const cached = this.cache.get<KnowledgeGraph>(cacheKey);
      if (cached) return cached;
    
      const graph = await this.loadGraph();
    
      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()))
      );
    
      const filteredEntityNames = new Set(filteredEntities.map(e => e.name));
      const filteredRelations = graph.relations.filter(r =>
        filteredEntityNames.has(r.from) && filteredEntityNames.has(r.to)
      );
    
      const result = {
        entities: filteredEntities,
        relations: filteredRelations,
      };
    
      this.cache.set(cacheKey, result);
      return result;
    }
  • index.ts:1070-1080 (registration)
    Tool registration in the list of tools returned by ListToolsRequestSchema, 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 CallToolRequestSchema handler that invokes the searchNodes method with tool arguments and formats response.
    case "search_nodes":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.searchNodes(args.query as string), null, 2) }] };
  • Input schema definition specifying the 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"],
    },
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 key behaviors such as whether it's read-only or mutative, what permissions are required, how results are returned (e.g., pagination, sorting), or any rate limits. This leaves significant gaps for an agent to understand operational traits.

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 that efficiently conveys the core purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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 in a knowledge graph with no annotations and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., read-only nature, result format) and doesn't compensate for the absence of structured data, making it inadequate for full agent understanding.

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 well-documented in the schema. The description adds minimal value beyond the schema by mentioning the query is used to match against 'entity names, types, and observation content', which slightly elaborates on the schema's description. This meets the baseline of 3 for high schema coverage.

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 with a specific verb ('Search') and resource ('nodes in the knowledge graph'), and it specifies the search scope ('based on a query'). However, it doesn't explicitly differentiate from sibling tools like 'find_similar_errors' or 'read_graph', which might also involve searching or reading operations in the knowledge graph context.

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 when to prefer 'search_nodes' over siblings like 'find_similar_errors' (which might search for errors) or 'read_graph' (which might retrieve graph data without query-based filtering), nor does it specify any prerequisites or exclusions for usage.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/T1nker-1220/memories-with-lessons-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server