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remember

Store text data in memory. Omit session_id for permanent graph memory; include it for fast session caching.

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
dataset_nameNo
session_idNo
custom_promptNo
Behavior4/5

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

With no annotations, the description discloses behavioral traits: two modes, entity extraction for permanent, fast for session, and custom_prompt relevance. It does not mention destructive actions or authentication needs, but covers essential behavior.

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 sections, front-loaded with the main purpose, and every sentence adds value. It is appropriately sized for the tool's complexity.

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?

Given the complexity of two modes, four parameters, and no output schema, the description is complete. It explains the dual behavior, parameter interactions, and default behaviors thoroughly.

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 adds meaning for all parameters: data content, default dataset name logic, session_id toggling cache mode, and custom_prompt applicability. This fully compensates for the lack of schema descriptions.

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 stores data in memory, and explains two modes based on session_id. It distinguishes from sibling tools like 'forget' and 'recall' by specifying it is for storage.

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 guidance on when to use session_id versus permanent memory, but does not explicitly mention when not to use this tool or when to use alternatives like cognify_file.

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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