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memory_recall

Retrieve stored project information and insights by searching with queries or project IDs to access previous analyses, recommendations, configurations, and interactions.

Instructions

Recall memories about a project or topic

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query or project ID
typeNoType of memory to recall
limitNoMaximum number of memories to return

Implementation Reference

  • The core handler function that implements the memory_recall tool. It initializes the memory manager, performs a search based on the query, optional type filter, and limit, then returns the matching memories with metadata.
    export async function handleMemoryRecall(args: {
      query: string;
      type?: string;
      limit?: number;
    }): Promise<any> {
      const manager = await initializeMemory();
    
      const searchOptions: any = {
        sortBy: "timestamp",
        limit: args.limit || 10,
      };
    
      if (args.type && args.type !== "all") {
        searchOptions.type = args.type;
      }
    
      const memories = await manager.search({}, searchOptions);
    
      return {
        query: args.query,
        type: args.type || "all",
        count: memories.length,
        memories: memories.map((m: any) => ({
          id: m.id,
          type: m.type,
          timestamp: m.timestamp,
          data: m.data,
          metadata: m.metadata,
        })),
      };
    }
  • The registration of the memory_recall tool within the exported memoryTools array, including name, description, and input schema.
    {
      name: "memory_recall",
      description: "Recall memories about a project or topic",
      inputSchema: {
        type: "object",
        properties: {
          query: {
            type: "string",
            description: "Search query or project ID",
          },
          type: {
            type: "string",
            enum: [
              "analysis",
              "recommendation",
              "deployment",
              "configuration",
              "interaction",
              "all",
            ],
            description: "Type of memory to recall",
          },
          limit: {
            type: "number",
            description: "Maximum number of memories to return",
            default: 10,
          },
        },
        required: ["query"],
      },
    },
  • The input schema definition for the memory_recall tool, specifying the expected parameters: query (required), type (enum), and limit.
    inputSchema: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "Search query or project ID",
        },
        type: {
          type: "string",
          enum: [
            "analysis",
            "recommendation",
            "deployment",
            "configuration",
            "interaction",
            "all",
          ],
          description: "Type of memory to recall",
        },
        limit: {
          type: "number",
          description: "Maximum number of memories to return",
          default: 10,
        },
      },
      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 full burden. It mentions 'recall' but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, how results are returned (e.g., format, pagination), 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.

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 appropriately sized and front-loaded, making it easy 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 no annotations and no output schema, the description is incomplete for a tool with 3 parameters. It doesn't explain what 'memories' are, how they're structured, or what the return values look like. For a tool in a memory-focused context with many siblings, more detail 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?

Schema description coverage is 100%, so the schema already documents all parameters (query, type, limit). The description adds no meaning beyond what's in the schema—it doesn't explain how 'query' relates to 'project or topic' or clarify the 'type' enum values. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Recall memories about a project or topic' states a general purpose (verb 'recall' + resource 'memories') but is vague about what 'memories' are and how they differ from other tools. It doesn't distinguish from siblings like 'memory_contextual_search' or 'memory_similar', which likely have overlapping functions.

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?

No guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites, exclusions, or compare to sibling tools (e.g., 'memory_contextual_search'), leaving the agent with no context for 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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