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asd-noor

Memory Engine MCP Server

by asd-noor
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
save_memoryA

Save a memory to the long-term storage.

Args:
    category: The category of the memory (e.g., "architecture", "preference", "bug_fix")
    topic: A short descriptive title for the memory
    content: The detailed memory/decision text

Returns:
    A dictionary with status, doc_id, topic, and category
delete_memoryA

Delete a memory by ID.

Args:
    doc_id: The ID of the memory to delete

Returns:
    A dictionary with status and message
update_memoryA

Update a memory by ID.

Args:
    doc_id: The ID of the memory to update
    category: New category (optional)
    topic: New topic (optional)
    content: New content (optional)

Returns:
    A dictionary with status and updated details
verify_memoryA

Mark a memory as verified, updating its last_verified timestamp to now.

Use this when you've confirmed the memory is still accurate and up-to-date.
This helps track memory freshness and prevents hallucinations from outdated information.

Args:
    doc_id: The ID of the memory to verify

Returns:
    A dictionary with status and message
query_memoryA

Query memories using semantic and keyword search.

Args:
    query: Natural language search string
    top_k: Number of results to return (default: 3)

Returns:
    A list of matching memories with similarity scores

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
get_usage_guidelinesMemory Engine usage guidelines for AI agents. Provides comprehensive documentation on: - When to save memories (DO's and DON'Ts) - How to structure memories (category, topic, content) - How to query effectively - Best practices and common patterns - Search features and capabilities Returns: The complete usage guidelines as markdown text
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored across 5 tools

Disambiguation5/5

Each tool targets a distinct memory operation: save (create), delete, update, verify, and query (search). There is no overlap in purpose, so an agent can easily select the right tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., save_memory, delete_memory, update_memory), making the API predictable and easy to navigate.

Tool Count5/5

With exactly 5 tools, the server is well-scoped for a memory management domain, covering essential operations without unnecessary complexity.

Completeness5/5

The server provides full CRUD coverage (save, query, update, delete) plus a verify operation for freshness. No critical lifecycle steps are missing for long-term memory storage.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources