In-depth architectural comparison of the Shodh Memory and Context First MCP 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
Shodh Memory
Knowledge & Memory · Local stdio
Quality: 69/100 (Great) | Auth: No auth required
Context First MCP
Knowledge & Memory · Local stdio
Quality: 52/100 (Good) | Auth: No auth required
Verdict Summary: Choose Shodh Memory if you need specialized Knowledge & Memory tools running via a local process. Choose Context First MCP 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 Shodh Memory 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).
Cognitive memory for AI agents with Hebbian learning, 3-tier architecture, and knowledge graphs. Single 15MB binary, runs offline on edge devices.
Session memory, context health monitoring, reasoning quality, and truthfulness verification MCP server with 37 tools and tiered memory storage. npx -y context-first-mcp
Category & Scope
Tools & Capabilities Breakdown
Shodh Memory Tools (38)
remember
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.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Shodh Memory is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Context First MCP belongs to Knowledge & Memory using local stdio subprocess. Select Shodh Memory when you need capabilities focused on knowledge & memory and Context First MCP when you require tools for knowledge & memory.
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.
+26 more tools listed on main page
Context First MCP Tools (36)
context_loop
One-call orchestrator.** Runs 8 stages (ingest→recap→conflict→ambiguity→entropy→abstention→discovery→synthesis) and returns a single `directive` with `action`, `contextHealth` score, extracted facts, and suggested next tools
recap_conversation
Extracts hidden intent, key decisions, and produces consolidated state summaries
detect_conflicts
Compares new input against ground truth; surfaces contradictions
check_ambiguity
Identifies underspecified requirements and generates clarifying questions
verify_execution
Validates whether tool outputs actually achieved the stated goal
entropy_monitor
Proxy-entropy scoring via lexical diversity, contradiction density, hedge frequency, and n-gram repetition (ERGO)
abstention_check
5-dimension confidence scoring — abstains with questions rather than hallucinating (RLAAR)
detect_drift
Detects conversation drift from the original intent
check_depth
Evaluates response depth against question complexity
get_state
Retrieve confirmed facts and task status
set_state
Lock in ground truth — subsequent conflict checks run against these values