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.
npx agentmods add instructions/arman-tech/spatial-memory-mcp/claude-mdgit clone --depth 1 https://github.com/arman-tech/spatial-memory-mcpWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00915 | $0.00915 |
| Opus 5 | $0.00458 | $0.00458 |
| Sonnet 5 | $0.00183 | $0.00183 |
| Haiku 4.5 | $0.00092 | $0.00092 |
Grade A, and why
spatial-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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Memory MCP - Contributor Guide
This document helps AI assistants (and human contributors) work effectively on this codebase.
Project Overview
spatial-memory-mcp is a persistent semantic memory MCP server for LLMs. It provides vector-based memory storage with spatial navigation capabilities using LanceDB and sentence-transformers.
Architecture
spatial_memory/
├── server.py # MCP server, tool handlers, instructions injection
├── factory.py # Dependency injection, service instantiation
├── config.py # Settings (Pydantic), environment configuration
├── core/ # Core infrastructure
│ ├── database.py # LanceDB wrapper, CRUD operations
│ ├── embeddings.py # Sentence-transformer embedding service
│ ├── models.py # Pydantic data models (Memory, etc.)
│ ├── errors.py # Exception hierarchy
│ ├── validation.py # Input validation, security checks
│ ├── security.py # Security utilities
│ ├── db_*.py # Database utilities (search, indexes, migrations)
│ └── spatial_ops.py # SLERP, vector operations
├── services/ # Business logic layer
│ ├── memory.py # Core memory operations (remember, recall, forget)
│ ├── spatial.py # Spatial operations (journey, wander, regions)
│ ├── lifecycle.py # Decay, reinforce, consolidate, extract
│ ├── export_import.py# Import/export functionality
│ └── utility.py # Stats, namespaces, health
├── adapters/ # External service adapters
├── ports/ # Interface definitions
└── tools/ # MCP tool definitions
Key Commands
# Run tests (unit tests only by default)
pytest tests/ -v
# Run all tests including integration
pytest tests/ -v -m ""
# Run integration tests only
pytest tests/ -v -m integration
# Type checking
mypy spatial_memory/
# Linting
ruff check spatial_memory/ tests/
# Format code
ruff format spatial_memory/ tests/
# Run the server directly
python -m spatial_memory
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.
- 2d ago First seen · 103 lines · 915 tokens per session scan A 4d21ac7e556c
spatial-memory-mcp CLAUDE.md is an instructions file published in the GitHub repository arman-tech/spatial-memory-mcp (1 stars, last pushed 4mo ago), licensed MIT. It adds 915 tokens to every session, about $0.0046 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 2026-08-31.
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