MCP Architecture

Purpose

Design the overall architecture and organization of Model Context Protocol servers with multiple tools (10+ tools). Provide patterns for grouping related tools, naming conventions for discoverability, and structural approaches that scale as MCP servers grow.

When to Use

Apply these patterns when:

• Designing an MCP server with 10 or more tools
• Reorganizing an existing MCP for better discoverability
• Planning tool relationships and data flow
• Structuring tools for progressive discovery
• Creating consistent naming across related tools

Core Principles

1. Logical Grouping

Group tools by domain, workflow, or data type rather than technical implementation.

Example: Code Search MCP

Tool	Group	Purpose
search	query	Find code patterns
get_definition	lookup	Get symbol definition
find_references	lookup	Find symbol usages
get_context	enrichment	Get full context for symbol
info	discovery	Enumerate available tools

Sparse table format - Human readable, shows relationships at a glance.

JSON array format for automation:

{
  "tool_groups": [
    {
      "name": "query",
      "tools": ["search"],
      "purpose": "Initial discovery operations"
    },
    {
      "name": "lookup",
      "tools": ["get_definition", "find_references"],
      "purpose": "Detailed symbol information"
    }
  ]
}

2. Naming Conventions

Use consistent verb-noun patterns that indicate:

• Action: What the tool does (search, get, find, list, create)
• Target: What it operates on (file, symbol, process, session)

Good naming patterns:

Query tools:     search_*, find_*, list_*
Lookup tools:    get_*, fetch_*, retrieve_*
Creation tools:  create_*, start_*, init_*
Management:      update_*, delete_*, stop_*
Discovery:       info, help, describe_*

Examples:

Tool Name	Pattern	Clear Purpose
search_code	verb_noun	✓
get_definition	verb_noun	✓
code_search	noun_verb	✗ Ambiguous order
find	verb_only	✗ Too vague
get	verb_only	✗ Get what?

3. Tool Relationships

Define explicit relationships between tools using token/ID systems for cross-tool references.

Example: Browser Integration MCP

proxy_start
  ↓ (generates: proxy_id)
proxy_status
  ↑ (consumes: proxy_id)
proxy_log
  ↑ (consumes: proxy_id)
  ↓ (generates: request_id)
proxy_replay
  ↑ (consumes: request_id)

Sparse table representation:

Tool	Generates	Consumes	Relationship
proxy_start	proxy_id	-	Root
proxy_status	-	proxy_id	Status query
proxy_log	request_id	proxy_id	Log retrieval
proxy_replay	-	request_id	Request replay

This shows data flow and tool dependencies at a glance.

4. Server Metadata

Structure server metadata for clarity and automation.

Minimal metadata:

{
  "name": "code-search",
  "version": "1.0.0",
  "description": "Lightning-fast semantic code search and analysis"
}

Comprehensive metadata:

{
  "name": "code-search",
  "version": "1.0.0",
  "description": "Lightning-fast semantic code search and analysis",
  "tool_count": 12,
  "tool_groups": ["query", "lookup", "enrichment", "discovery"],
  "progressive_discovery": true,
  "has_info_tool": true,
  "token_systems": ["result_id", "symbol_id"],
  "max_response_tokens": 2000,
  "capabilities": {
    "search": true,
    "definitions": true,
    "references": true,
    "call_hierarchy": true
  }
}

Automation flags like progressive_discovery and has_info_tool help AI agents understand how to use the server effectively.

Organizational Patterns

Pattern 1: Layered Discovery

Organize tools in progressive layers of detail:

Layer 1: Discovery - info tool Layer 2: Overview - search, list_* tools Layer 3: Details - get_*, find_* tools Layer 4: Deep Dive - analyze_*, trace_* tools

Example structure:

Layer	Tools	Token Cost	Use When
1	info	~50	Starting exploration
2	search_code	~100	Finding candidates
3	get_definition	~200	Understanding specific symbol
4	trace_callers	~500	Deep analysis

Pattern 2: Workflow Grouping

Organize tools around common workflows:

Code Search Workflows:

Workflow: Find Implementation
  1. search_code("function name")
     → Generates: result_id[]
  2. get_definition(result_id)
     → Returns: Full definition

Workflow: Understand Usage
  1. search_code("class name")
  2. find_references(result_id)
  3. get_context(reference_id)

Document workflows in server metadata or info tool output.

Pattern 3: Domain Separation

For servers handling multiple domains, use prefixes:

Code domain:     code_search, code_definition, code_references
File domain:     file_search, file_read, file_stats
Project domain:  project_info, project_structure, project_deps

Sparse table:

Prefix	Domain	Tool Count
code_*	Code analysis	5
file_*	File operations	3
project_*	Project metadata	4

Scaling Strategies

When to Split an MCP Server

Consider splitting when:

• Tool count exceeds 20
• Distinct domains with different deployment requirements
• Different authentication/authorization needs
• Tools have different performance characteristics

Example: Split recommendation

Before (25 tools):
  unified-dev-server
    - Code tools (8)
    - Browser tools (7)
    - Process tools (6)
    - Session tools (4)

After (3 servers):
  code-search-server (8 tools)
  browser-proxy-server (7 tools)
  process-manager-server (10 tools, combining process + session)

When to Keep Tools Together

Keep tools in one server when:

• They share common data/state
• Cross-tool workflows are frequent
• Combined tool count < 15
• Deployment complexity isn't worth the split

Naming Examples

Good Tool Names (Following Patterns)

Code Search Domain:

search_code          - Search code by pattern
get_definition       - Get symbol definition by ID
find_references      - Find symbol references
list_symbols         - List all symbols in file
analyze_dependencies - Analyze code dependencies

Process Management Domain:

start_process   - Start a new process
get_status      - Get process status by ID
stop_process    - Stop running process
list_processes  - List all processes
tail_output     - Get recent process output

Browser Integration Domain:

start_proxy      - Start reverse proxy
get_errors       - Get JavaScript errors
capture_screenshot - Capture browser screenshot
inject_script    - Inject JavaScript code
measure_performance - Get performance metrics

Poor Tool Names (Avoid These)

search          - Too vague (search what?)
get             - Too vague (get what?)
find            - Too vague (find what?)
process         - Noun, not verb-noun
code            - Not clear what it does
run             - Ambiguous (run what?)

Architecture Documentation

Document architecture in server metadata, README, or info tool output.

Example info tool output (sparse table):

Tool Groups
===========

Query Tools (Fast, <100 tokens)
  search_code      - Search code patterns
  search_files     - Search file names

Lookup Tools (Medium, ~200 tokens)
  get_definition   - Get symbol definition
  find_references  - Find symbol usages

Analysis Tools (Slow, ~500 tokens)
  trace_callers    - Trace call hierarchy
  analyze_deps     - Analyze dependencies

Use get_help(tool_name) for detailed tool documentation.

JSON format for automation:

{
  "tool_groups": [
    {
      "name": "Query Tools",
      "performance": "fast",
      "avg_tokens": 100,
      "tools": [
        {"name": "search_code", "description": "Search code patterns"},
        {"name": "search_files", "description": "Search file names"}
      ]
    }
  ],
  "discovery": {
    "info_tool": "info",
    "help_tool": "get_help"
  }
}

Additional Resources

Reference Files

For detailed patterns and advanced techniques, consult:

• references/patterns.md - Comprehensive organizational patterns
• references/naming.md - Extended naming conventions and examples
• references/scaling.md - Strategies for scaling MCP servers

Examples

Working examples in examples/:

• code-search-architecture.json - Complete code search MCP structure
• browser-proxy-architecture.json - Browser integration MCP structure
• process-manager-architecture.json - Process management MCP structure

Quick Reference

When architecting an MCP server:

1. Group logically - By domain, workflow, or data type
2. Name consistently - Use verb-noun patterns
3. Define relationships - Document token/ID systems
4. Layer discovery - From overview to deep dive
5. Document structure - In metadata or info tool
6. Use sparse tables - For human readability
7. Provide JSON - For automation

Tool organization checklist:

• Tools grouped by logical domain
• Consistent verb-noun naming
• Token/ID relationships documented
• Progressive discovery layers defined
• Server metadata includes automation flags
• Info tool provides architecture overview
• Workflows documented
• Scaling strategy considered

Focus on discoverability and progressive access to prevent overwhelming users with too many tools at once.