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modelcontextprotocol

Knowledge Graph Memory Server

delete_observations

Remove specific observations from entities in the Knowledge Graph Memory Server, enabling precise data management and memory updates across interactions.

Instructions

Delete specific observations from entities in the knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deletionsYes

Implementation Reference

  • The handler function that executes the 'delete_observations' tool logic. It calls the KnowledgeGraphManager's deleteObservations method and returns a success response.
    async ({ deletions }) => {
      await knowledgeGraphManager.deleteObservations(deletions);
      return {
        content: [{ type: "text" as const, text: "Observations deleted successfully" }],
        structuredContent: { success: true, message: "Observations deleted successfully" }
      };
    }
  • Input and output schema (Zod) for the delete_observations tool defining the deletions parameter structure and success response.
    {
      title: "Delete Observations",
      description: "Delete specific observations from entities in the knowledge graph",
      inputSchema: {
        deletions: z.array(z.object({
          entityName: z.string().describe("The name of the entity containing the observations"),
          observations: z.array(z.string()).describe("An array of observations to delete")
        }))
      },
      outputSchema: {
        success: z.boolean(),
        message: z.string()
      }
    },
  • The complete registration of the 'delete_observations' tool using server.registerTool, including name, schema, and handler.
    // Register delete_observations tool
    server.registerTool(
      "delete_observations",
      {
        title: "Delete Observations",
        description: "Delete specific observations from entities in the knowledge graph",
        inputSchema: {
          deletions: z.array(z.object({
            entityName: z.string().describe("The name of the entity containing the observations"),
            observations: z.array(z.string()).describe("An array of observations to delete")
          }))
        },
        outputSchema: {
          success: z.boolean(),
          message: z.string()
        }
      },
      async ({ deletions }) => {
        await knowledgeGraphManager.deleteObservations(deletions);
        return {
          content: [{ type: "text" as const, text: "Observations deleted successfully" }],
          structuredContent: { success: true, message: "Observations deleted successfully" }
        };
      }
    );
  • The core helper method in KnowledgeGraphManager that performs the actual deletion of observations from entities in the graph by loading, filtering, and saving the graph.
    async deleteObservations(deletions: { entityName: string; observations: string[] }[]): Promise<void> {
      const graph = await this.loadGraph();
      deletions.forEach(d => {
        const entity = graph.entities.find(e => e.name === d.entityName);
        if (entity) {
          entity.observations = entity.observations.filter(o => !d.observations.includes(o));
        }
      });
      await this.saveGraph(graph);
    }
Behavior2/5

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

With no annotations provided, the description carries full burden but only states the action 'delete' without behavioral details. It doesn't disclose whether deletions are permanent, require specific permissions, have rate limits, or what happens if observations don't exist. For a destructive operation, this is a significant gap.

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, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to scan and understand 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 tool's destructive nature, no annotations, no output schema, and low schema coverage, the description is inadequate. It lacks critical information about safety, permissions, error handling, and return values, which are essential for an AI agent to use this tool correctly.

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 description must compensate but only vaguely mentions 'specific observations' without explaining parameter structure. It doesn't clarify what 'entityName' refers to or how 'observations' are identified, leaving the schema to do all the work. Baseline 3 is appropriate as it minimally adds context.

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 'delete' and the resource 'observations from entities in the knowledge graph', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_entities' or 'delete_relations', which would require more specific language about scope.

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 'delete_entities' or 'delete_relations'. It doesn't mention prerequisites, such as whether entities must exist first, or specify use cases like cleaning up erroneous data versus bulk removal.

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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