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MikSkrzyp

identity-storage-mcp

by MikSkrzyp

memory_store

Stores memories from AI agent interactions, supporting episodic events, semantic facts, and procedural steps for persistent recall across sessions.

Instructions

Store a memory. You MUST call this after every non-trivial turn. Choose the type based on what you are saving:

  • episodic: an event that happened — 'fixed the login bug in auth.py', 'user asked for a tic-tac-toe game', 'refactored auth module to use JWT'. Concrete actions and outcomes.

  • semantic: a durable fact that stays true — 'user prefers Python 3.12', 'project uses pytest', 'auth uses JWT', 'user communicates in Polish'. Knowledge about the user or project.

  • procedural: a how-to with steps — 'run tests with pytest -x', 'deploy via npm run build && rsync', 'start dev server: python -m backend.main'. Steps to accomplish something.

Set confidence below 1.0 for inferences, assumptions, or guesses. Use tags for filtering (e.g. project name, topic). Episodic payload keys: session_id, agent, task, outcome, parent_id, metadata. Store one memory per distinct thing. ALWAYS skip idle chat, greetings, and trivial responses. Forgetting to store = permanent loss of the session.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
created_atYes
memory_typeYes
Behavior4/5

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

With no annotations, the description reveals important behavior: storing one memory per distinct thing, the consequence of forgetting ('permanent loss'), and specific payload keys for episodic type. It does not mention rate limits or auth, but covers key operational traits.

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 clear lead sentence and bullet points for types. It is somewhat lengthy but every sentence adds value, avoiding redundancy.

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 complexity of the nested input schema and the presence of an output schema, the description is fairly complete. It explains the types, usage rules, and key parameters. It could briefly mention the return value, but the output schema likely covers that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite the context listing 0% schema description coverage (which seems contradicted by the schema itself), the description adds significant meaning: explaining each memory type, when to set confidence below 1.0, and the role of tags. This goes beyond the schema's brief 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 'Store a memory.' and explains when to call it ('after every non-trivial turn'). It distinguishes between memory types (episodic, semantic, procedural) with concrete examples, differentiating from sibling tools like memory_recall and memory_search.

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 this tool ('after every non-trivial turn'), what types to use, and what to skip ('idle chat, greetings, and trivial responses'). It does not explicitly mention alternatives, but the sibling context is provided separately.

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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