MCP
- Pages
- 7
- References
- 6
- Related Terms
- 8
Definition
MCP is an open protocol for connecting LLM applications to external tools, data sources, prompts, and contextual resources.
Background
Introduced by Anthropic in 2024, Model Context Protocol reframes integrations around a common client-server interface instead of bespoke tool connectors for each AI application.
Position
It sits between AI agents, tool access, authentication, and memory systems. Knowledge Graph and Graphiti are adjacent knowledge backends; OAuth, OIDC, and PKCE are adjacent authorization concepts.
Distinctions
- It is distinct from other MCP expansions such as Microsoft Certified Professional.
- MCP is not a model; it is a connection protocol used by model-based applications.
Primary source-backed reference selected for this concept.
Sources
- Model Context Protocol specification Official
- Model Context Protocol documentation Official
Page Context
- AI/LLM/Ontology/Organizational Memory
1. Executive Summary Rather than being a "knowledgeable entity," modern LLMs are probabilistic pattern generators that learn from large volumes of language, code, images, and be...
Quote: AI/LLM/Ontology/Organizational Memory ai-systems
- MCP and Agent Skills for Browser E2E Testing
MCP and Agent Skills for Browser E2E Testing 1. Executive Summary As of May 2026, the right way to let an AI agent run browser E2E checks is not simply "add a browser MCP server...
Quote: MCP and Agent Skills for Browser E2E Testing developer-tools
- AI agent memory platform created with Graphiti and MCP
1. Executive Summary By combining Graphiti and MCP, AI agents can be provided with "memory that persists across conversation sessions" and "a standard point of contact from exte...
Quote: AI agent memory platform created with Graphiti and MCP ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
How LLM Training, Fine-Tuning, RAG, and Agents Differ 1. Executive Summary The most common mistake in LLM discussions is to treat pretraining, fine-tuning, prompting, RAG, tool ...
Quote: How LLM Training, Fine-Tuning, RAG, and Agents Differ ai-systems
- OAuth 2.1 PKCE Flow and MCP Authentication Authorization Practical Guide
OAuth 2.1 PKCE flow and MCP authentication authorization practical guide 1. Executive Summary In practice, MCP server authentication and authorization can be easily understood a...
Quote: OAuth 2.1 PKCE Flow and MCP Authentication Authorization Practical Guide developer-tools
- Research Log: LLM Training, Fine-Tuning, RAG, and Agents
Research Log: LLM Training, Fine-Tuning, RAG, and Agents Environment - model: gpt-5.4-mini - skill: research-report - prompt source: ops/codex/prompts/daily-issue-research.md Re...
Quote: Research Log: LLM Training, Fine-Tuning, RAG, and Agents ai-systems
Pages
- AI/LLM/Ontology/Organizational Memory
An integrated report that connects the history of AI research, LLM, symbolic grounding, Ontology, Graphiti, and MCP to practical decisions for utilizing organizational knowledge.
ai-systems
- MCP and Agent Skills for Browser E2E Testing
A comparison of Playwright MCP, Chrome DevTools MCP, Webwright, AgentBrowser, Browserbase/Stagehand, and browser-use for browser E2E verification with logged-in sessions.
developer-tools
- AI agent memory platform created with Graphiti and MCP
A practical report that organizes the design theory, implementation decisions, and major alternative services for long-term memory that combines Graphiti's temporal knowledge graph and MCP.
ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- OAuth 2.1 PKCE Flow and MCP Authentication Authorization Practical Guide
For beginners, we will organize the relationship between the MCP server, OIDC provider, OAuth client, Claude-based client, and OpenAPI-only API from the perspective of communication flow and settings.
developer-tools
- Research Log: LLM Training, Fine-Tuning, RAG, and Agents
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- LLM Training, Fine-Tuning, RAG, and Agents: Source Notes
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems