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What is Model Context Protocol (MCP)?

Definition

The Model Context Protocol (MCP) is an open-source standard providing a uniform architecture for AI applications to connect with external data sources, development tools, and enterprise environments safely and reliably.

How Memora uses Model Context Protocol (MCP)

Memora supports the Model Context Protocol through its MCP server, which gives Claude Code, Cursor and other MCP clients access to team knowledge from Slack, GitHub, Jira and other connected sources.

Interactive MCP Quick-Starter

Connect your AI assistant to Memora's persistent memory layer. Runs locally over stdio.

claude_desktop_config.json
{
  "mcpServers": {
    "memora-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@memora/mcp-server@latest"
      ],
      "env": {
        "MEMORA_API_KEY": "YOUR_MEMORA_API_KEY",
        "MEMORA_WORKSPACE_ID": "ws_enterprise_main"
      }
    }
  }
}
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Related concepts

Terms that connect to Model Context Protocol (MCP).

View the full glossary

AI Context

AI Context is the dynamic, multi-modal layer of situational information—including code symbols, historical decision records, team discussions, and compliance constraints—injected into Large Language Model prompts to ensure domain-accurate, enterprise-specific reasoning.

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AI Context Window

An AI Context Window is the defined structural capacity boundary that limits the total volume of raw data tokens (text characters, code fragments, formatting structures) a language model can simultaneously read, process, and analyze in a single computational loop.

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Context Engineering

Context Engineering is the systematic software engineering discipline of dynamically retrieving, filtering, synthesizing, compressing, and structuring ground-truth enterprise data to provide AI models and agents with the exact information needed to execute tasks accurately and reliably.

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Agentic AI

Agentic AI represents an advanced operational paradigm where artificial intelligence models operate as semi-autonomous or fully autonomous software agents.

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Agentic RAG

Agentic RAG (Retrieval-Augmented Generation) is an advanced AI architecture where autonomous agents actively formulate retrieval strategies, evaluate retrieved context quality, dynamically route queries across vector and graph indexes, and execute iterative tool loops to reduce unsupported answers in enterprise decision-making.

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AI Agent

An AI Agent is an autonomous operational software entity driven by large cognitive reasoning models that can perceive its corporate environment, maintain memory states, make logical decisions, design step-by-step action paths, and invoke external APIs or software tools to execute specific business goals with minimal human intervention.

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