Glossary

Definitions of MCP-related terms and concepts.

About This Document

The MCP ecosystem involves many specialized terms and abbreviations. This document serves as a centralized reference for the definitions of terms used throughout this documentation set.

If you are new to this documentation, you can deepen your understanding by referring here when encountering unfamiliar terms. It also serves to unify terminology interpretation as a common language within teams.

Protocols and Standards

MCP (Model Context Protocol)

An open protocol developed by Anthropic for connecting AI models with external tools and resources.

Features:
- JSON-RPC based
- Provides Tools, Resources, and Prompts
- Often described as "USB for AI"

Related: MCP Server, MCP Client, MCP Host

A2A (Agent-to-Agent Protocol)

An inter-agent communication protocol proposed by Google and donated to the Linux Foundation.

Features:
- Standardizes collaboration between agents
- Complementary relationship with MCP (MCP=tool connection, A2A=inter-agent)
- Over 150 companies have announced support

Related: Agent Card, Task Management

RFC (Request for Comments)

Technical standard documents published by IETF for internet technologies.

Examples:
- RFC 6455: WebSocket Protocol
- RFC 3161: Timestamp Protocol
- RFC 9110: HTTP Semantics

Related: IETF, MUST/SHOULD/MAY

MCP Architecture

MCP Host

An application that embeds an MCP Client and provides the user interface.

Examples:
- Claude Code
- Claude.ai
- Cursor
- VS Code (via extensions)

MCP Client

The protocol layer that handles communication with MCP Servers.

Responsibilities:
- Server discovery and startup
- Connection management
- JSON-RPC communication
- Error handling

Typically embedded in the Host; developers rarely interact with it directly.

MCP Server

A service that provides Tools, Resources, and Prompts.

Responsibilities:
- Tool definition and execution
- Providing access to Resources
- Providing Prompt templates

Examples:
- rfcxml-mcp (RFC parsing)
- deepl-mcp (translation)
- xcomet-mcp-server (quality evaluation)

Tool

An executable function provided by an MCP Server.

Examples:
- get_rfc_structure (rfcxml-mcp)
- translate-text (deepl-mcp)
- xcomet_evaluate (xcomet-mcp-server)

Components:
- name
- description
- inputSchema

Resource

Data or files provided by an MCP Server.

Examples:
- Files in the filesystem
- Database records
- External API data

Identified using URI format.

Claude Code Specific

Custom Subagent

An AI assistant specialized for specific tasks that can be defined within Claude Code.

Definition locations:
- .claude/agents/xxx.md (project)
- ~/.claude/agents/xxx.md (user)

Features:
- Independent context
- Tool restrictions possible
- Clear role definition

Note: Not a "replacement" for MCP Client, but rather a "higher layer"

Skill

Static knowledge and guidelines that can be referenced in Claude Code.

Definition locations:
- .claude/skills/xxx/SKILL.md (project)
- ~/.claude/skills/xxx/SKILL.md (user)

Features:
- Markdown format
- No execution capability (reference only)
- Low context consumption

Use cases: Best practices, workflow definitions, coding conventions

CLAUDE.md

An instruction file for Claude placed at the project root.

Contents:
- Project overview
- List of MCPs in use
- Coding conventions
- Workflow instructions

Requirement Levels

MUST / MUST NOT

Mandatory requirements in RFCs. Non-compliance constitutes a specification violation.

Example: "A TCP implementation MUST support simultaneous open attempts"

SHOULD / SHOULD NOT

Recommended requirements in RFCs. May be violated with valid justification.

Example: "Implementations SHOULD use exponential backoff"

MAY

Optional requirements in RFCs. Implementation is at the discretion of the developer.

Example: "A client MAY provide additional metadata"

Quality Evaluation

xCOMET

A neural metric for evaluating translation quality.

Features:
- Score from 0-1 (higher is better quality)
- Error span detection
- Can evaluate without reference translations

Error Severity

The severity level of errors detected by xCOMET.

Levels:
- critical: Severe (meaning reversal, mistranslation)
- major: Moderate (unnatural expressions)
- minor: Minor (style issues)

AI Design Patterns

RAG (Retrieval-Augmented Generation)

A technique that retrieves external documents via vector search and injects relevant information into the LLM's prompt.

How it works:
1. Split documents into chunks → Vectorize → Store in DB
2. Vectorize the user's question
3. Retrieve related chunks via similarity search
4. Inject chunks into prompt for LLM to generate answer

Strengths: Can find relevant information from large volumes of unstructured text
Weaknesses: Context lost through chunking, doesn't understand structure

Related: Embedding, Vector DB, Chunk

Difference from MCP: See concepts/04-ai-design-patterns.md

Embedding

Converting text into numerical vectors (arrays of hundreds to thousands of dimensions). Semantically similar texts are placed close together in vector space. The foundational technology behind RAG's vector search.

Vector Database

A specialized database for storing and searching embedded vector data. Provides fast similarity search using cosine similarity and other metrics.

Examples: Pinecone, Weaviate, Chroma, pgvector

Chunk

A small fragment created by splitting a document. In RAG, documents are split into chunks before vectorization. The chunk size and splitting method affect search accuracy.

Prompt Engineering

A technique for controlling output quality solely through input prompt design, without changing model parameters. Includes techniques such as Zero-shot, Few-shot, and Chain-of-Thought.

GraphRAG

A technique that combines standard RAG with knowledge graphs, leveraging entity relationships for search and generation. Particularly effective for relational questions like "How is A related to B?"

Fine-tuning

A technique that further trains an LLM's parameters on domain-specific data. If RAG is "external memory," Fine-tuning is closer to "rewriting internal knowledge."

Agentic AI

A pattern where an LLM autonomously plans, invokes tools, and solves problems through multiple steps. MCP is one of the foundational technologies that enables this pattern.

Related: MCP, Subagent, A2A

Other Terms

World Model

An agent's internal representation of environmental structure and physical laws. The foundational concept that enables agents to predict "what will happen next" and simulate the consequences of their actions.

Contexts:
- Robotics / autonomous driving: Internal models of physics (gravity, inertia, collision)
- LLMs: Implicit understanding of causal relationships and commonsense world behavior
- Reinforcement learning: Environment dynamics models (model-based RL)

In this site, World Models are primarily discussed in the context of Physical AI. While implicitly present in information-space agents, they are an essential element for agents that operate in the physical world.

Reference: Yann LeCun — A Path Towards Autonomous Machine Intelligence (2022)

Authoritative Reference Sources

Authoritative information sources for maintaining consistency in AI decisions.

Hierarchy:
1. International standards and regulations (MUST comply)
2. Industry standards and de facto standards (SHOULD comply)
3. Organization/project conventions (local)
4. Best practices (recommended)

Democratization of Knowledge

Lowering barriers to accessing specialized knowledge.

In the MCP context:
- Limited number of people can read specifications
  → AI references them via MCP and provides in an understandable format for anyone
- Accurate information-based development becomes possible
  without relying on expensive consultants or specialists

AI-Driven Development

A development methodology that utilizes AI as an "intelligent assistant" throughout the entire development process, not just for code generation.

≠ Having AI write code
= Utilizing AI throughout all processes while humans focus on judgment and creativity

Over-MCPization

A state where too many MCP servers are added unnecessarily, overwhelming the context window.

Symptoms:
- Tool definitions constantly consume context
- Increased startup overhead
- 70k problem (performance degradation from too many tools)

Countermeasures:
- Migrate to Skills where possible
- Limit MCPs per project

Agent Card

Self-introduction information for agents in the A2A protocol.

Location: /.well-known/agent.json

Contents:
- Agent name
- Endpoint
- Provided skills
- Authentication method

Abbreviation List

Abbreviation	Full Name	Description
RAG	Retrieval-Augmented Generation	Search-augmented generation technique
MCP	Model Context Protocol	Protocol for connecting AI and tools
A2A	Agent-to-Agent Protocol	Inter-agent communication protocol
RFC	Request for Comments	IETF technical standard documents
IETF	Internet Engineering Task Force	Internet technology standardization body
W3C	World Wide Web Consortium	Web standardization body
WHATWG	Web Hypertext Application Technology Working Group	Standardization body for HTML, etc.
API	Application Programming Interface	Interface between applications
JSON-RPC	JSON Remote Procedure Call	JSON-based RPC protocol
ADR	Architecture Decision Record	Architecture decision documentation
TLS	Transport Layer Security	Communication encryption protocol
TSA	Time Stamp Authority	Timestamp authority