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remember

Store content in memory. Without a session ID, builds a knowledge graph for permanent recall; with one, saves to fast session cache.

Instructions

Store data in memory.

Two modes depending on whether session_id is provided:

Without session_id (permanent memory): Runs the full add + cognify
pipeline to ingest data and build the knowledge graph.

With session_id (session memory): Stores the data in the session
cache only. Fast, no entity extraction. Omit session_id when the
content should be stored as permanent graph memory.

Parameters
----------
data : str
    The data to store (text content).
dataset_name : str, optional
    Target dataset name. Defaults to the current MCP client's
    agent-scoped dataset (e.g. "cursor_vscode_memory"), or
    "main_dataset" if no client identity is detected.
session_id : str, optional
    Session ID. When set, stores in session cache only.
custom_prompt : str, optional
    Custom prompt for entity extraction (permanent mode only).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
session_idNo
dataset_nameNo
custom_promptNo
Behavior3/5

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

With no annotations, the description must convey behavioral traits. It explains the two processing modes (pipeline vs. cache) but does not mention idempotency, data size limits, or potential side effects (e.g., overwriting). Adequate but not comprehensive.

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

Conciseness4/5

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

The description is well-structured with a summary line, mode explanations, and a parameters section. It is not overly verbose, though slightly longer than necessary. Conciseness is good.

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

Completeness3/5

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

The description covers modes and parameters adequately but lacks return value specification, error conditions, and data persistence details. Without an output schema, these gaps reduce completeness.

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 fully carries parameter semantics. It lists all four parameters with clear explanations, including defaults and mode-specific usage (e.g., custom_prompt only in permanent mode).

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 purpose: 'Store data in memory.' It distinguishes between two modes (permanent vs. session memory), differentiating this tool from siblings like recall (retrieval) and forget (deletion).

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 explains when to use each mode: omit session_id for permanent memory (full pipeline) and provide session_id for fast session cache. It omits explicit comparisons to sibling tools, but the modes are well-explained.

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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