MCP Server Development Guide

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services.

Core Design Principles

Build for Workflows, Not Just APIs

Principle	Why
Consolidate operations	Single tool for complete tasks
Return high-signal data	Agents have limited context
Provide format options	"concise" vs "detailed" modes
Use human-readable IDs	Not technical codes
Make errors actionable	Guide toward correct usage

Key concept: Don't just wrap API endpoints. Design tools that enable complete workflows agents actually need.

Development Phases

Phase 1: Research

Step	Action
Study MCP Protocol	Read `modelcontextprotocol.io/llms-full.txt`
Study SDK docs	Python or TypeScript SDK README
Study target API	Read ALL available documentation
Create implementation plan	Before writing code

Phase 2: Design

Decision	Options
Language	Python (FastMCP) or TypeScript
Tool granularity	Atomic vs workflow-oriented
Response format	JSON, Markdown, or both
Error handling	What errors can occur, how to recover

Phase 3: Implementation

Component	Purpose
Input validation	Pydantic (Python) or Zod (TypeScript)
Tool descriptions	Clear, with examples
Error messages	Include suggested next steps
Response formatting	Consistent across tools

Phase 4: Testing

Critical: MCP servers are long-running processes. Never run directly in main process.

Approach	How
Evaluation harness	Recommended
tmux session	Run server separately
Timeout wrapper	`timeout 5s python server.py`
MCP Inspector	Official debugging tool

Tool Annotations

Annotation	Meaning	Default
readOnlyHint	Doesn't modify state	false
destructiveHint	Can cause damage	true
idempotentHint	Repeated calls safe	false
openWorldHint	Interacts externally	true

Key concept: Annotations help the LLM decide when and how safely to use tools.

Input Design

Validation Patterns

Pattern	Use Case
Required fields	Core parameters
Optional with defaults	Convenience parameters
Enums	Limited valid values
Min/max constraints	Numeric bounds
Pattern matching	Format validation (email, URL)

Parameter Naming

Good	Bad	Why
`user_email`	`e`	Self-documenting
`limit`	`max_results_to_return`	Concise but clear
`include_archived`	`ia`	Descriptive boolean

Response Design

Format Options

Format	Use Case
JSON	Programmatic use, structured data
Markdown	Human readability, reports
Hybrid	JSON in markdown code blocks

Response Guidelines

Guideline	Why
~25,000 token limit	Context constraints
Truncate with indicator	Don't silently cut
Support pagination	`limit` and `offset` params
Include metadata	Total count, has_more

Error Handling

Error Message Structure

Element	Purpose
What failed	Clear description
Why it failed	Root cause if known
How to fix	Suggested next action
Example	Correct usage

Key concept: Error messages should guide the agent toward correct usage, not just diagnose problems.

Quality Checklist

Code Quality

Check	Description
No duplicated code	Extract shared logic
Consistent formats	Similar ops return similar structure
Full error handling	All external calls wrapped
Type coverage	All inputs/outputs typed
Comprehensive docstrings	Every tool documented

Tool Quality

Check	Description
Clear descriptions	Model knows when to use
Good examples	In docstring
Sensible defaults	Reduce required params
Consistent naming	Group related with prefixes

Best Practices

Practice	Why
One tool = one purpose	Clear mental model
Comprehensive descriptions	LLM selection accuracy
Include examples in docstrings	Show expected usage
Return actionable errors	Enable self-correction
Test with actual LLM	Real-world validation
Version your server	Track compatibility

Resources

MCP Protocol: https://modelcontextprotocol.io/
Python SDK: https://github.com/modelcontextprotocol/python-sdk
TypeScript SDK: https://github.com/modelcontextprotocol/typescript-sdk

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