mem0-mcp

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search_memories

Search memories using natural language queries to retrieve relevant preferences, decisions, and patterns from past sessions.

Instructions

Semantically search memories for relevant context.

Use this at the start of tasks to recall relevant preferences,
decisions, patterns, and facts from previous sessions.

Args:
    query: Natural language search query (e.g., "TypeScript preferences",
           "server architecture", "coding style")

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": "search_memories"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 27, 2026

TDQS

A4.3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates a read operation ('search') but does not disclose any behavioral traits such as result limits, ordering, or whether it modifies state. The semantic search method is implied but not detailed.

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 concise—three sentences plus a parameter docstring—with no wasted words. It front-loads the purpose and usage, and every sentence adds value.

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?

Given that an output schema exists, the description does not need to explain return values. It covers purpose, usage, and parameter semantics well. However, it lacks information on result limits or scope (e.g., whether it searches all sessions or recent ones).

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%, but the description thoroughly explains the query parameter with a natural language definition and concrete examples (e.g., 'TypeScript preferences'). This adds significant meaning beyond the schema's type and required fields.

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 action 'semantically search' and the resource 'memories', and distinguishes from siblings by specifying semantic search vs. other types. It also provides a concrete use case: recalling context from previous sessions.

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 explicitly advises using this tool at the start of tasks to recall preferences, decisions, patterns, and facts. It gives clear context but does not explicitly state when not to use it or mention alternatives, though sibling tool names imply alternatives.

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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