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Recall Memories

recall

Retrieve relevant memories for user queries to fetch prior context, preferences, and facts before responding.

Instructions

Retrieve relevant memories for a given query. Call at the start of user turns to fetch prior context, preferences, and facts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe user message or search query to recall memories for

Implementation Reference

  • Registration and handler implementation for the 'recall' tool.
    server.registerTool(
      "recall",
      {
        title: "Recall Memories",
        description:
          "Retrieve relevant memories for a given query. Call at the start of user turns to fetch prior context, preferences, and facts.",
        inputSchema: {
          query: z
            .string()
            .describe("The user message or search query to recall memories for"),
        },
      },
      async ({ query }) => {
        try {
          const client = await getRemoteClient();
          const result = await client.callTool({
            name: "recall",
            arguments: { query },
          });
          return {
            content: (result.content as Array<{ type: "text"; text: string }>) || [
              { type: "text", text: "No memories found" },
            ],
          };
        } catch (error) {
          const message = error instanceof Error ? error.message : String(error);
          return {
            content: [
              { type: "text" as const, text: `Error recalling memories: ${message}` },
            ],
            isError: true,
          };
        }
      },
    );
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses what types of memories are retrieved (context, preferences, facts) and implies relevance ranking, but omits safety profile (read-only status), failure modes (no memories found), or return format details.

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?

Two sentences with zero waste: first defines the action, second provides temporal usage guidance. Information density is optimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a single-parameter retrieval tool without output schema. Description compensates partially by specifying what content is fetched (preferences, facts), though it could clarify return structure or empty-result behavior.

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 coverage is 100%, establishing a baseline of 3. The description mentions 'query' but adds minimal semantic detail beyond the schema's definition ('The user message or search query'). No clarification needed given comprehensive schema.

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?

Clear verb ('Retrieve') and resource ('memories') with scope ('relevant...for a given query'). However, it does not explicitly differentiate from sibling 'advanced_augmentation', though the functions appear distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to invoke ('Call at the start of user turns') and explains the value proposition ('fetch prior context, preferences, and facts'). Lacks explicit 'when not to use' guidance or alternative comparisons.

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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