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Memory Recall

localnest_memory_recall
Read-only

Retrieve relevant local memories for tasks or queries using semantic search and project context to support AI agents with codebase access.

Instructions

Recall the most relevant local memories for a task or query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
root_pathNo
project_pathNo
branch_nameNo
topicNo
featureNo
kindNoknowledge
limitNo
response_formatNojson

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaNo

Implementation Reference

  • The handler function for 'localnest_memory_recall' which calls 'memory.recall' and normalizes the result.
    async ({ query, root_path, project_path, branch_name, topic, feature, kind, limit }) => normalizeMemoryRecallResult(
      await memory.recall({
        query,
        rootPath: root_path,
        projectPath: project_path,
        branchName: branch_name,
        topic,
        feature,
        kind,
        limit
      }),
      query
    )
  • Tool registration of 'localnest_memory_recall' within 'registerMemoryWorkflowTools'.
    registerJsonTool(
      ['localnest_memory_recall'],
      {
        title: 'Memory Recall',
        description: 'Recall the most relevant local memories for a task or query.',
        inputSchema: {
          query: z.string().min(1),
          root_path: z.string().optional(),
          project_path: z.string().optional(),
          branch_name: z.string().optional(),
          topic: z.string().optional(),
          feature: z.string().optional(),
          kind: MEMORY_KIND_SCHEMA.optional(),
          limit: z.number().int().min(1).max(50).default(10)
        },
        annotations: {
          readOnlyHint: true,
          destructiveHint: false,
          idempotentHint: false,
          openWorldHint: false
        }
      },
      async ({ query, root_path, project_path, branch_name, topic, feature, kind, limit }) => normalizeMemoryRecallResult(
        await memory.recall({
          query,
          rootPath: root_path,
          projectPath: project_path,
          branchName: branch_name,
          topic,
          feature,
          kind,
          limit
        }),
        query
      )
    );
Behavior2/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, which the description confirms by using the verb 'Recall'. However, the description does not explain why idempotentHint=false for a read operation (possibly due to dynamic relevance ranking), nor does it clarify what constitutes 'local' memory scope or how relevance is determined.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single-sentence description is compact and front-loaded with the core action, but it is inappropriately brief given the tool's complexity (9 parameters with 0% schema coverage). It sacrifices necessary documentation for brevity.

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?

Despite having an output schema (removing the burden to describe return values), the description remains incomplete. With 9 parameters including multiple filtering dimensions and no schema descriptions, the tool requires detailed parameter guidance that is entirely absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description fails to compensate. While it implicitly references the 'query' parameter via 'task or query', it completely omits the other 8 parameters including critical filters (root_path, project_path, branch_name, topic, feature) and output controls (kind, limit, response_format).

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 states the tool recalls 'the most relevant local memories' and specifies the input context ('for a task or query'), giving a clear verb and resource. However, it fails to distinguish from siblings like localnest_memory_get or localnest_memory_list, which likely perform direct lookups rather than relevance-based retrieval.

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 is provided on when to use this tool versus alternatives like memory_get, memory_list, or memory_events. Given the rich ecosystem of memory tools available, the description should explicitly state this performs semantic/relevance-based search versus ID-based retrieval.

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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