How do I use Semantic Mesh Memory (SEM) MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Semantic Mesh Memory (SEM) MCP Server Find any semantic contradictions in my memory about the product roadmap." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

@sem/mcp-server

Coherent Memory for LLM Agents

A memory layer that detects contradictions and surfaces them for review. Unlike append-only logs or RAG retrieval, this system models beliefs as nodes in a constraint network where semantic similarity implies expected agreement.

What it does

When you store beliefs, the system:

Embeds them locally (Xenova/all-MiniLM-L6-v2, no API calls)
Auto-links to similar existing beliefs
Computes strain using hybrid geometric-logical energy
Surfaces contradictions when beliefs conflict

Installation

# Install globally
npm install -g @sem/mcp-server

# Or run via npx
npx @sem/mcp-server

Claude Code / MCP Configuration

Add to your mcp_servers.json:

{
  "mcpServers": {
    "sem-memory": {
      "command": "npx",
      "args": ["@sem/mcp-server"],
      "env": {
        "SEM_DATA_DIR": "/path/to/your/memory"
      }
    }
  }
}

Tools

`memory_add`

Add a belief to memory.

memory_add({
  belief: "The user prefers dark mode",
  source: "settings conversation",
  confidence: 0.9
})
// Returns: { id, autoLinked, contradictions }

`memory_query`

Search for relevant beliefs.

memory_query({ topic: "user preferences", limit: 5 })
// Returns: { beliefs: [...], contradictions: [...] }

Each belief includes:

relevance: How relevant to the query
strain: Coherence tension (higher = needs attention)
status: 'stable' | 'needs_review' | 'high_tension'

`memory_contradictions`

Get all current contradictions.

memory_contradictions()
// Returns pairs of conflicting beliefs

`memory_link`

Explicitly define a relationship between beliefs.

memory_link({
  sourceId: "sem_123",
  targetId: "sem_456",
  relation: "contradicts"  // or: supersedes, elaborates, related, caused, caused_by
})

`memory_forget`

Remove a belief.

memory_forget({ id: "sem_123" })

`memory_stats`

Get memory health metrics.

memory_stats()
// Returns: { totalBeliefs, totalEdges, stable, needsReview, highTension, energy... }

How Strain Works

The system uses a hybrid energy model:

Logical Energy (E_logic)

Positive constraints: Penalize disagreement between related beliefs
Negative constraints: Penalize co-acceptance of contradicting beliefs

Geometric Energy (E_geom)

Spring energy based on embedding distance vs. rest length
Beliefs that drift apart semantically create tension

Total Energy: E_total = E_logic + λ * E_geom

High-strain beliefs are flagged as needs_review or high_tension.

Data Storage

By default, beliefs are stored in .sem-data/memory-index.jsonl. Set SEM_DATA_DIR env var to customize.

Theory

Based on Thagard & Verbeurgt's "Coherence as Constraint Satisfaction" - coherence is modeled as maximizing satisfaction of positive/negative constraints between elements.

See: Semantic Mesh Memory (paper)