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itseasy21

Knowledge Graph Memory Server

add_observations

Add new observations to existing entities in a knowledge graph to maintain updated memory across conversations.

Instructions

Add new observations to existing entities in the knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
observationsYes

Implementation Reference

  • The core handler function in KnowledgeGraphManager that executes the add_observations tool logic: loads the graph, adds new unique observations to specified entities, persists changes, and returns results.
    async addObservations(observations: { entityName: string; contents: string[] }[]): Promise<{ entityName: string; addedObservations: string[] }[]> {
      const graph = await this.loadGraph();
      const results = observations.map(o => {
        const entity = graph.entities.find(e => e.name === o.entityName);
        if (!entity) {
          throw new Error(`Entity with name ${o.entityName} not found`);
        }
        const newObservations = o.contents.filter(content => !entity.observations.includes(content));
        entity.observations.push(...newObservations);
        return { entityName: o.entityName, addedObservations: newObservations };
      });
      await this.saveGraph(graph);
      return results;
    }
  • The input schema definition for the add_observations tool, specifying the structure of observations array with entityName and contents.
    inputSchema: {
      type: "object",
      properties: {
        observations: {
          type: "array",
          items: {
            type: "object",
            properties: {
              entityName: { type: "string", description: "The name of the entity to add the observations to" },
              contents: {
                type: "array",
                items: { type: "string" },
                description: "An array of observation contents to add"
              },
            },
            required: ["entityName", "contents"],
          },
        },
      },
      required: ["observations"],
    },
  • index.ts:332-356 (registration)
    Registration of the add_observations tool in the ListToolsRequestSchema handler, including name, description, and input schema.
    {
      name: "add_observations",
      description: "Add new observations to existing entities in the knowledge graph",
      inputSchema: {
        type: "object",
        properties: {
          observations: {
            type: "array",
            items: {
              type: "object",
              properties: {
                entityName: { type: "string", description: "The name of the entity to add the observations to" },
                contents: {
                  type: "array",
                  items: { type: "string" },
                  description: "An array of observation contents to add"
                },
              },
              required: ["entityName", "contents"],
            },
          },
        },
        required: ["observations"],
      },
    },
  • index.ts:518-519 (registration)
    Dispatch/registration in the CallToolRequestSchema switch statement that invokes the handler for add_observations tool calls.
    case "add_observations":
      return { content: [{ type: "text", text: JSON.stringify(await knowledgeGraphManager.addObservations(args.observations as { entityName: string; contents: string[] }[]), null, 2) }] };
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool adds observations to existing entities, implying a mutation operation, but doesn't address permissions, side effects, error handling, or response format. This leaves significant gaps for a tool that modifies data.

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 that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function.

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?

For a mutation tool with no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what 'observations' are in this context, how they're structured, or what happens after addition (e.g., success indicators, error cases).

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 description doesn't mention any parameters, and schema description coverage is 0%, so it adds no semantic value beyond what the schema provides. However, with only 1 parameter (an array of observation objects), the baseline is 3 as the schema alone might be minimally sufficient for 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 action ('Add new observations') and target ('to existing entities in the knowledge graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_entities' or 'update_entities' beyond the focus on observations.

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' (for new entities) or 'update_entities' (which might also modify observations). It mentions 'existing entities' as a prerequisite but doesn't clarify exclusion criteria or compare with sibling tools.

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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