memex

store_memory

Store and persist conversation memories for AI agents. Supports episodic, semantic, and procedural memory types with adjustable importance weights.

Instructions

Store a memory for an agent/user pair. Content is embedded locally (ONNX)
and persisted to Postgres. Returns the memory ID on success.

Args:
    agent_id:     Unique identifier for the agent storing the memory.
    user_id:      Unique identifier for the user this memory belongs to.
    content:      The text to remember (max 8000 chars).
    memory_type:  'episodic' (events/conversations), 'semantic' (facts/preferences),
                  or 'procedural' (workflows). Default: episodic.
    importance:   Weight multiplier 0.0–2.0. Use >1.0 for critical facts. Default: 1.0.

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": {
      "agent_id": "<agent_id>",
      "user_id": "<user_id>",
      "content": "<content>"
    },
    "name": "store_memory"
  }
}

Input Schema

Name	Required	Default
`agent_id`	Yes
`user_id`	Yes
`content`	Yes
`memory_type`	No	episodic
`importance`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 4, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations provided, the description fully discloses key behaviors: local embedding via ONNX, persistence to Postgres, response format (memory ID), content length limit (8000 chars), memory type list, and importance range with usage hint.

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 yet complete: a one-line summary followed by a clear bullet list of parameters. There is no fluff; each 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 the presence of an output schema and the complexity (5 parameters, 3 required, defaults), the description covers essential behavioral and parameter info. It mentions the output but could be slightly enhanced by noting potential error conditions, though not required.

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?

Since schema description coverage is 0%, the description must compensate, and it does so thoroughly. It explains each parameter's purpose (agent_id, user_id, content with max chars), enum-like values for memory_type with defaults, and importance with range and recommendation.

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 ('Store a memory'), the subject ('for an agent/user pair'), and the outcome ('returns the memory ID'). It differentiates from sibling tools (count, delete, search) by being the store operation.

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 details all parameters and defaults, providing implicit usage context (e.g., memory_type options, importance hint). However, it lacks explicit guidance on when to use this tool over alternatives like search_memories or delete_memory.

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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