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recall

Search stored long-term memories to find information relevant to a query. Use this to retrieve past context, preferences, or facts before answering when they matter.

Instructions

Search long-term memory for information relevant to a query.

Read-only, fully local semantic search. Call this before answering when the
user refers to past context, preferences, or previously-shared facts.

Args:
    query: What to look for (a question or topic).
    limit: Max memories to return (1-50, default 5).
    user_id: Namespace to search (default "default").

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "recall"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`query`	Yes
`user_id`	No	default

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 17, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses 'Read-only, fully local semantic search,' indicating no side effects and privacy (local). This is meaningful additional context beyond just 'search' though it could mention potential limitations like semantic match quality.

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 well-structured with a purpose line, usage guidance, and a clear Args list. It is front-loaded with the key function and every sentence earns its place.

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

Completeness5/5

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

The description covers the tool's purpose, usage context, safety (read-only, local), and all parameter details. Since an output schema exists, return value details are not needed. It is complete for a simple retrieval tool.

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

Parameters5/5

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

Schema coverage is 0%, so the description must compensate. It does so thoroughly: query is 'What to look for (a question or topic)', limit is 'Max memories to return (1-50, default 5)', and user_id is 'Namespace to search (default "default")'—adding semantics and defaults not present in the schema.

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

Purpose5/5

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

The description clearly states the tool's function: 'Search long-term memory for information relevant to a query.' It uses a specific verb and resource, and the read-only nature distinguishes it from sibling tools (remember, forget, reset_memories) that modify memory.

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?

The description provides explicit when-to-use guidance: 'Call this before answering when the user refers to past context, preferences, or previously-shared facts.' It lacks explicit when-not-to-use or named alternative tools, but the context is clear enough.

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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