Jarvis Orb vs Shodh Memory — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Jarvis Orb vs Shodh Memory
In-depth architectural comparison of the Jarvis Orb and Shodh Memory MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Jarvis Orb
Knowledge & Memory · Local stdio
Quality: 48/100 (Fair) | Auth: No auth required
Shodh Memory
Knowledge & Memory · Local stdio
Quality: 69/100 (Great) | Auth: No auth required
Verdict Summary: Choose Jarvis Orb if you need specialized Knowledge & Memory tools running via a local process. Choose Shodh Memory if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Jarvis Orb when:
You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
You have access to required keys: PYTHONPATH.
Primary tools included: Four-tier memory with automatic classification, Temporal scoring with 30-day half-life decay, Contradiction detection and superseded-memory filtering.
Jarvis Orb is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Shodh Memory belongs to Knowledge & Memory using local stdio subprocess. Select Jarvis Orb when you need capabilities focused on knowledge & memory and Shodh Memory when you require tools for knowledge & memory.
Store a memory for future recall. Use this to remember important information, decisions, user preferences, project context, or anything you want to recall later.
recall
Search memories AND todos using semantic similarity. Returns both relevant memories and matching todos. Use this to find past experiences, decisions, context, or pending work. Modes: 'semantic' (vector similarity), 'associative' (graph traversal), 'temporal' (time-based retrieval), 'hybrid' (combined), 'spatial' (geo-location based), 'mission' (mission context), 'action_outcome' (reward-based learning).
recall_by_tags
Find memories by tags. Returns memories matching ANY of the provided tags. Useful for finding memories by category (e.g., 'tool:Edit', 'file:src/main.rs', 'source:hook', 'error', 'session-summary').
context_summary
Get a condensed summary of recent learnings, decisions, and context. Use this at the start of a session to quickly understand what you've learned before.
list_memories
List all stored memories
forget
Delete a specific memory by ID
memory_stats
Get statistics about stored memories
verify_index
Verify vector index integrity - diagnose orphaned memories that are stored but not searchable. Returns health status and count of orphaned memories.
repair_index
Repair vector index by re-indexing orphaned memories. Use this when verify_index shows unhealthy status. Returns count of repaired memories.
backup_create
Create a backup of all memories. Returns backup metadata including ID, size, and checksum. Backups are stored locally and can be restored later.
backup_list
List all available backups for this user. Returns backup history with IDs, timestamps, and sizes.
backup_verify
Verify backup integrity using SHA-256 checksum. Use to check if a backup is corrupted before restoring.