vector-memory-mcp CLAUDE.md

vector-memory-mcp CLAUDE.md is an instructions file for coding agents from Xsaven/vector-memory-mcp. It costs 1,021 tokens per session, scanned A, original, MIT.

Project instructions for the vector-memory-mcp repository, a Python MCP server for semantic memory search using SQLite and sqlite-vec. They describe its dependencies, project structure, and Claude Desktop setup.

In plain words
What is it for?
They are for maintaining and extending the vector-memory-mcp codebase, including its memory store, embeddings, security checks, dependencies, and Claude Desktop configuration.
Why use it?
They give an agent the repository's technical context before it changes code. This reduces guesswork about the Python version, storage layer, embedding model, and main files.

Instructions file

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/xsaven/vector-memory-mcp/claude-md
Clone the repo
git clone --depth 1 https://github.com/Xsaven/vector-memory-mcp

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README.md
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Per session 1,021 This file is loaded in full into every session.
When invoked 1,021 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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ModelPer sessionOnce invoked
Fable 5 $0.01021 $0.01021
Opus 5 $0.00511 $0.00511
Sonnet 5 $0.00204 $0.00204
Haiku 4.5 $0.00102 $0.00102

Measured 4d ago against content hash b91590f39b13, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

vector-memory-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Vector Memory MCP Server

Project Overview

MCP (Model Context Protocol) Server для векторної пам'яті з використанням sqlite-vec для семантичного пошуку.

Technology Stack

  • Python: 3.11.8 (requires >= 3.10)
  • Package Manager: uv (сучасний Python package manager)
  • Database: SQLite 3.43.2 + sqlite-vec extension
  • Embeddings: sentence-transformers/all-MiniLM-L6-v2 (384-dimensional vectors)
  • MCP Framework: FastMCP >= 0.3.0

Key Dependencies

  • mcp>=0.3.0 - Model Context Protocol framework
  • sqlite-vec>=0.1.6 - Vector search extension для SQLite
  • sentence-transformers>=2.2.2 - Embedding models

Project Structure

  • main.py - Entry point with uv script configuration
  • requirements.txt - Python dependencies for pip/venv compatibility
  • pyproject.toml - Modern Python project configuration
  • .python-version - Python version specification (3.11)
  • claude-desktop-config.example.json - Claude Desktop configuration template
  • src/models.py - Data models and configuration
  • src/security.py - Security validation and sanitization
  • src/memory_store.py - Vector memory storage operations
  • src/embeddings.py - Embedding generation
  • memory/ - SQLite database storage directory

How to Run

Standalone

# On Apple Silicon (M1/M2/M3) - use run-arm64.sh script
./run-arm64.sh --working-dir /your/working/directory

# With custom memory limit (default: 10,000 entries)
./run-arm64.sh --working-dir /your/working/directory --memory-limit 100000

# Alternative with conda Python (has SQLite extensions support)
~/miniconda3/envs/vector-mcp/bin/python main.py --working-dir ./ --memory-limit 100000

# Using uv (requires Python with SQLite extensions support)
uv run main.py --working-dir ./ --memory-limit 100000

Configuration Options

  • --working-dir - Working directory for memory database (required, default: current directory)
  • --memory-limit - Maximum number of memory entries (optional, default: 10,000)
    • Minimum: 1,000 entries
    • Maximum: 10,000,000 entries
    • Recommended for large projects: 100,000-1,000,000

Read the full file on GitHub · 105 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 105 lines · 1,021 tokens per session scan A b91590f39b13

Subscribe to this mod's changes

vector-memory-mcp CLAUDE.md is an instructions file published in the GitHub repository Xsaven/vector-memory-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 1,021 tokens to every session, about $0.0051 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.