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shaneholloman

mcp-knowledge-graph

aim_memory_store

Store and organize persistent memories about people, projects, and concepts with custom categories. Create separate knowledge graphs for different contexts like work or personal.

Instructions

Store new memories. Use this to remember people, projects, concepts, or any information worth persisting.

AIM (AI Memory) provides persistent memory for AI assistants. The 'aim_memory_' prefix groups all memory tools together.

WHAT'S STORED: Memories have a name, type (person/project/concept/etc.), and observations (facts about them).

DATABASES: Use the 'context' parameter to organize memories into separate graphs:

  • Leave blank: Uses the master database (default for general information)

  • Any name: Creates/uses a named database ('work', 'personal', 'health', 'research', etc.)

  • New databases are created automatically - no setup required

  • IMPORTANT: Use consistent, simple names - prefer 'work' over 'work-stuff'

STORAGE LOCATIONS: Files are stored as JSONL (e.g., memory.jsonl, memory-work.jsonl):

  • Project-local: .aim directory in project root (auto-detected if exists)

  • Global: User's configured --memory-path directory

  • Use 'location' parameter to override: 'project' or 'global'

RETURNS: Array of created entities.

EXAMPLES:

  • Master database (default): aim_memory_store({entities: [{name: "John", entityType: "person", observations: ["Met at conference"]}]})

  • Work database: aim_memory_store({context: "work", entities: [{name: "Q4_Project", entityType: "project", observations: ["Due December 2024"]}]})

  • Master database in global location: aim_memory_store({location: "global", entities: [{name: "John", entityType: "person", observations: ["Met at conference"]}]})

  • Work database in project location: aim_memory_store({context: "work", location: "project", entities: [{name: "Q4_Project", entityType: "project", observations: ["Due December 2024"]}]})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional memory context. Defaults to master database if not specified. Use any descriptive name ('work', 'personal', 'health', 'basket-weaving', etc.) - new contexts created automatically.
locationNoOptional storage location override. 'project' forces project-local .aim directory, 'global' forces global directory. If not specified, uses automatic detection.
entitiesYes
Behavior5/5

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

The description thoroughly discloses behavioral traits: it explains the structure of memories (name, type, observations), the use of 'context' for databases, storage mechanisms (JSONL files, .aim directory, global), and return value (array of created entities). Since no annotations are provided, the description fully covers transparency.

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 clear sections (WHAT'S STORED, DATABASES, etc.) and front-loaded with the core purpose. While concise for the complexity, it could be slightly trimmed without losing information.

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 tool's complexity (3 parameters, nested array) and absence of output schema, the description covers all necessary aspects: purpose, parameters, usage patterns, storage details, return values, and examples. It is complete and self-contained.

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?

The description adds significant meaning beyond the input schema. For 'context', it explains master database and naming conventions. For 'location', it clarifies project/global and automatic detection. For 'entities', it details the nested object structure. Schema coverage is high, but the description enhances understanding with practical guidance.

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 purpose: 'Store new memories... remember people, projects, concepts, or any information worth persisting.' It specifies the verb (store) and resource (memories), and the sibling tools (like aim_memory_add_facts or aim_memory_forget) have different purposes, making it distinguishable.

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 guidance on when to use the tool (to remember information) and includes examples for different scenarios (master database, named context, location). However, it does not explicitly state when not to use it or directly reference sibling tools for 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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