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mcp-memory

mcp-memory

by oneKn8
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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remember

Store text content as a memory with semantic embeddings for later recall. Use tags, project scope, and importance to organize and retrieve information across sessions.

Instructions

Store a memory for later recall. Content is embedded for semantic search.

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": {
      "content": "<content>"
    },
    "name": "remember"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNoOptional tags for filtering (e.g. ["architecture", "decision"]).
sourceNoOptional note about where this memory came from.
contentYesThe text content to remember.
projectNoProject scope for this memory (default: "global").
importanceNoPriority 1-5, where 5 is most important (default: 3).

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It mentions that content is embedded for semantic search, which is a key behavioral trait, but it does not disclose persistence, side effects, or any other behaviors beyond storage.

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 a single, front-loaded sentence. Every word is necessary and adds value, with no wasted content.

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?

Given the tool has an output schema (not shown but indicated), the description does not mention return values. It also does not guide usage of optional parameters like tags or importance, leaving some gaps for a complete understanding.

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

Parameters3/5

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

Input schema has 100% description coverage for parameters. The tool description adds no additional parameter meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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 uses a specific verb 'Store' and resource 'memory', clearly stating the action. It distinguishes from sibling tools like 'forget' (removal) and 'recall' (retrieval) by emphasizing storage for later recall.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when you want to store a memory for later recall via semantic search, but it does not explicitly state when to use this tool versus alternatives like 'list_memories' or 'recall'. No when-not or exclusion criteria are provided.

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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