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Mcp Server Best Practices

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jiaxiaojunQAQ
mcp-server-best-practices

Production-ready patterns and best practices for MCP servers - architecture, security, performance, and maintenance

Overview

PublisherjiaxiaojunQAQ
RepositorySkillJect
Skill namemcp-server-best-practices
Stars
79
Forks
8
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jiaxiaojunQAQ on GitHub. Read the source before you install it.

Installation

Install the Mcp Server Best Practices AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jiaxiaojunQAQ/SkillJect.git /tmp/SkillJect
mkdir -p .claude/skills
cp -r /tmp/SkillJect/data/skills_sample/mcp-best-practices .claude/skills/mcp-server-best-practices
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mcp Server Best Practices in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mcp Server Best Practices on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mcp Server Best Practices is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You are an expert in MCP server best practices, with comprehensive knowledge of production patterns, security, performance optimization, testing strategies, and maintainability.

Your Expertise

You guide developers on:

  • Architecture and design patterns
  • Security best practices
  • Performance optimization
  • Error handling strategies
  • Testing and quality assurance
  • Deployment and operations
  • Monitoring and observability
  • Maintenance and evolution

Architecture Patterns

Pattern 1: Layered Architecture

rust
// Layer 1: Transport (handled by rmcp)
// Layer 2: Service (your business logic)
// Layer 3: Domain (core logic)
// Layer 4: Infrastructure (external services)

mod transport {
    // Transport configuration
}

mod service {
    // MCP service implementation
    use crate::domain::*;
    use crate::infrastructure::*;

    #[tool(tool_box)]
    pub struct McpService {
        domain: Arc<DomainService>,
        repo: Arc<dyn Repository>,
    }
}

mod domain {
    // Core business logic
    pub struct DomainService {
        // Pure business logic
    }
}

mod infrastructure {
    // External integrations
    pub trait Repository: Send + Sync {
        async fn get(&self, id: &str) -> Result<Data>;
    }
}

Pattern 2: Hexagonal Architecture (Ports and Adapters)

rust
// Core domain (no external dependencies)
mod core {
    pub struct McpCore {
        // Business rules
    }

    // Ports (interfaces)
    pub trait DataPort: Send + Sync {
        async fn fetch(&self, id: &str) -> Result<Data>;
    }

    pub trait CachePort: Send + Sync {
        async fn get(&self, key: &str) -> Option<String>;
        async fn set(&self, key: &str, value: String);
    }
}

// Adapters (implementations)
mod adapters {
    use super::core::*;

    pub struct PostgresAdapter {
        pool: PgPool,
    }

    impl DataPort for PostgresAdapter {
        async fn fetch(&self, id: &str) -> Result<Data> {
            // Database implementation
        }
    }

    pub struct RedisAdapter {
        client: redis::Client,
    }

    impl CachePort for RedisAdapter {
        async fn get(&self, key: &str) -> Option<String> {
            // Redis implementation
        }
    }
}

// MCP Service uses ports, not concrete adapters
#[tool(tool_box)]
struct McpService {
    core: Arc<McpCore>,
    data: Arc<dyn DataPort>,
    cache: Arc<dyn CachePort>,
}

Pattern 3: Repository Pattern

rust
use async_trait::async_trait;

#[async_trait]
trait Repository<T>: Send + Sync {
    async fn get(&self, id: &str) -> Result<Option<T>>;
    async fn list(&self) -> Result<Vec<T>>;
    async fn create(&self, entity: &T) -> Result<String>;
    async fn update(&self, id: &str, entity: &T) -> Result<()>;
    async fn delete(&self, id: &str) -> Result<()>;
}

struct UserRepository {
    pool: PgPool,
}

#[async_trait]
impl Repository<User> for UserRepository {
    async fn get(&self, id: &str) -> Result<Option<User>> {
        sqlx::query_as!(User, "SELECT * FROM users WHERE id = $1", id)
            .fetch_optional(&self.pool)
            .await
            .map_err(Into::into)
    }

    // ... other methods
}

#[tool(tool_box)]
struct UserService {
    repo: Arc<UserRepository>,
}

#[tool(tool_box)]
impl UserService {
    #[tool(description = "Get user by ID")]
    async fn get_user(&self, id: String) -> Result<User, ServiceError> {
        self.repo.get(&id).await?
            .ok_or_else(|| ServiceError::NotFound(format!("User {}", id)))
    }
}

Error Handling

Comprehensive Error Types

rust
use thiserror::Error;

#[derive(Debug, Error)]
pub enum ServiceError {
    #[error("Resource not found: {0}")]
    NotFound(String),

    #[error("Invalid input: {field} - {message}")]
    InvalidInput { field: String, message: String },

    #[error("Permission denied: {0}")]
    PermissionDenied(String),

    #[error("Rate limit exceeded: {0}")]
    RateLimitExceeded(String),

    #[error("External service error: {service} - {message}")]
    ExternalServiceError { service: String, message: String },

    #[error("Database error: {0}")]
    DatabaseError(#[from] sqlx::Error),

    #[error("Serialization error: {0}")]
    SerializationError(#[from] serde_json::Error),

    #[error("Internal error: {0}")]
    Internal(String),
}

impl ServiceError {
    pub fn error_code(&self) -> &'static str {
        match self {
            Self::NotFound(_) => "NOT_FOUND",
            Self::InvalidInput { .. } => "INVALID_INPUT",
            Self::PermissionDenied(_) => "PERMISSION_DENIED",
            Self::RateLimitExceeded(_) => "RATE_LIMIT",
            Self::ExternalServiceError { .. } => "EXTERNAL_ERROR",
            Self::DatabaseError(_) => "DATABASE_ERROR",
            Self::SerializationError(_) => "SERIALIZATION_ERROR",
            Self::Internal(_) => "INTERNAL_ERROR",
        }
    }

    pub fn is_retryable(&self) -> bool {
        matches!(
            self,
            Self::ExternalServiceError { .. } | Self::DatabaseError(_)
        )
    }
}

Error Context and Recovery

rust
use anyhow::Context;

#[tool(tool_box)]
impl MyService {
    #[tool(description = "Fetch data with retry")]
    async fn fetch_with_retry(&self, id: String) -> Result<Data, ServiceError> {
        let mut attempts = 0;
        let max_attempts = 3;

        loop {
            attempts += 1;

            match self.fetch_data(&id).await {
                Ok(data) => return Ok(data),
                Err(e) if e.is_retryable() && attempts < max_attempts => {
                    tracing::warn!(
                        "Attempt {} failed: {}. Retrying...",
                        attempts,
                        e
                    );
                    tokio::time::sleep(Duration::from_millis(100 * attempts)).await;
                    continue;
                }
                Err(e) => {
                    return Err(e)
                        .context(format!("Failed after {} attempts", attempts))?;
                }
            }
        }
    }
}

Security

Input Validation

rust
use validator::{Validate, ValidationError};

#[derive(Debug, Deserialize, Validate, JsonSchema)]
struct CreateUserRequest {
    #[validate(length(min = 1, max = 100))]
    name: String,

    #[validate(email)]
    email: String,

    #[validate(length(min = 8))]
    password: String,

    #[validate(range(min = 18, max = 120))]
    age: u32,
}

#[tool(tool_box)]
impl UserService {
    #[tool(description = "Create user with validation")]
    async fn create_user(
        &self,
        #[tool(aggr)] req: CreateUserRequest,
    ) -> Result<User, ServiceError> {
        // Validate input
        req.validate()
            .map_err(|e| ServiceError::InvalidInput {
                field: "request".to_string(),
                message: e.to_string(),
            })?;

        // Additional business validation
        if self.repo.exists_by_email(&req.email).await? {
            return Err(ServiceError::InvalidInput {
                field: "email".to_string(),
                message: "Email already exists".to_string(),
            });
        }

        // Hash password
        let password_hash = hash_password(&req.password)?;

        // Create user
        let user = User {
            id: Uuid::new_v4().to_string(),
            name: req.name,
            email: req.email,
            password_hash,
            age: req.age,
        };

        self.repo.create(&user).await?;
        Ok(user)
    }
}

Authentication and Authorization

rust
use jsonwebtoken::{decode, DecodingKey, Validation};

#[derive(Debug, Deserialize)]
struct Claims {
    sub: String,  // user ID
    role: String,
    exp: usize,
}

struct AuthContext {
    user_id: String,
    role: String,
}

impl AuthContext {
    fn from_token(token: &str) -> Result<Self, ServiceError> {
        let key = DecodingKey::from_secret(SECRET.as_ref());
        let token_data = decode::<Claims>(token, &key, &Validation::default())
            .map_err(|_| ServiceError::PermissionDenied("Invalid token".to_string()))?;

        Ok(Self {
            user_id: token_data.claims.sub,
            role: token_data.claims.role,
        })
    }

    fn require_admin(&self) -> Result<(), ServiceError> {
        if self.role != "admin" {
            return Err(ServiceError::PermissionDenied(
                "Admin role required".to_string()
            ));
        }
        Ok(())
    }
}

#[tool(tool_box)]
struct SecureService {
    repo: Arc<UserRepository>,
}

#[tool(tool_box)]
impl SecureService {
    #[tool(description = "Delete user (admin only)")]
    async fn delete_user(
        &self,
        auth_token: String,
        user_id: String,
    ) -> Result<(), ServiceError> {
        let auth = AuthContext::from_token(&auth_token)?;
        auth.require_admin()?;

        self.repo.delete(&user_id).await?;
        Ok(())
    }
}

SQL Injection Prevention

rust
// ✅ Good: Use parameterized queries
async fn get_user(&self, id: &str) -> Result<User> {
    sqlx::query_as!(User, "SELECT * FROM users WHERE id = $1", id)
        .fetch_one(&self.pool)
        .await
        .map_err(Into::into)
}

// ❌ Bad: String concatenation
async fn get_user_unsafe(&self, id: &str) -> Result<User> {
    let query = format!("SELECT * FROM users WHERE id = '{}'", id);  // VULNERABLE!
    sqlx::query_as(&query)
        .fetch_one(&self.pool)
        .await
        .map_err(Into::into)
}

Performance Optimization

Connection Pooling

rust
use sqlx::postgres::PgPoolOptions;

async fn create_db_pool() -> Result<PgPool> {
    PgPoolOptions::new()
        .max_connections(20)
        .min_connections(5)
        .acquire_timeout(Duration::from_secs(10))
        .idle_timeout(Duration::from_secs(600))
        .connect(&database_url)
        .await
        .map_err(Into::into)
}

Caching Strategy

rust
use moka::future::Cache;

struct CachedService {
    inner: Arc<InnerService>,
    cache: Cache<String, Data>,
}

impl CachedService {
    fn new(inner: Arc<InnerService>) -> Self {
        let cache = Cache::builder()
            .max_capacity(10_000)
            .time_to_live(Duration::from_secs(3600))
            .time_to_idle(Duration::from_secs(600))
            .build();

        Self { inner, cache }
    }

    async fn get_data(&self, id: &str) -> Result<Data> {
        // Try cache
        if let Some(data) = self.cache.get(id).await {
            return Ok(data);
        }

        // Fetch from source
        let data = self.inner.fetch_data(id).await?;

        // Update cache
        self.cache.insert(id.to_string(), data.clone()).await;

        Ok(data)
    }
}

Async Best Practices

rust
// ✅ Good: Concurrent operations
async fn fetch_all_data(&self) -> Result<Vec<Data>> {
    let futures = ids.into_iter().map(|id| self.fetch_one(id));
    let results = futures_util::future::try_join_all(futures).await?;
    Ok(results)
}

// ❌ Bad: Sequential operations
async fn fetch_all_data_slow(&self) -> Result<Vec<Data>> {
    let mut results = Vec::new();
    for id in ids {
        results.push(self.fetch_one(id).await?);  // Blocks!
    }
    Ok(results)
}

// ✅ Good: Timeout for external calls
async fn fetch_with_timeout(&self, id: &str) -> Result<Data> {
    tokio::time::timeout(
        Duration::from_secs(30),
        self.external_api.fetch(id)
    )
    .await
    .map_err(|_| ServiceError::Timeout)?
    .map_err(Into::into)
}

Testing

Unit Testing

rust
#[cfg(test)]
mod tests {
    use super::*;
    use mockall::predicate::*;
    use mockall::mock;

    mock! {
        Repository {}

        #[async_trait]
        impl Repository<User> for Repository {
            async fn get(&self, id: &str) -> Result<Option<User>>;
            async fn create(&self, user: &User) -> Result<String>;
        }
    }

    #[tokio::test]
    async fn test_get_user_found() {
        let mut mock_repo = MockRepository::new();

        mock_repo
            .expect_get()
            .with(eq("123"))
            .returning(|_| Ok(Some(User {
                id: "123".to_string(),
                name: "Test".to_string(),
                email: "test@example.com".to_string(),
            })));

        let service = UserService {
            repo: Arc::new(mock_repo),
        };

        let result = service.get_user("123".to_string()).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_get_user_not_found() {
        let mut mock_repo = MockRepository::new();

        mock_repo
            .expect_get()
            .with(eq("999"))
            .returning(|_| Ok(None));

        let service = UserService {
            repo: Arc::new(mock_repo),
        };

        let result = service.get_user("999".to_string()).await;
        assert!(matches!(result, Err(ServiceError::NotFound(_))));
    }
}

Integration Testing

rust
#[cfg(test)]
mod integration_tests {
    use super::*;
    use testcontainers::*;

    #[tokio::test]
    async fn test_full_flow() {
        // Set up test database
        let docker = clients::Cli::default();
        let postgres = docker.run(images::postgres::Postgres::default());

        let pool = create_test_pool(&postgres).await;

        // Create service
        let repo = Arc::new(PostgresRepository::new(pool));
        let service = UserService { repo };

        // Test create
        let user = service.create_user(CreateUserRequest {
            name: "Test".to_string(),
            email: "test@example.com".to_string(),
            password: "password123".to_string(),
            age: 25,
        }).await.unwrap();

        // Test get
        let retrieved = service.get_user(user.id.clone()).await.unwrap();
        assert_eq!(retrieved.name, "Test");

        // Test delete
        service.delete_user(user.id).await.unwrap();
    }
}

Monitoring and Observability

Structured Logging

rust
use tracing::{info, error, warn, instrument};

#[instrument(skip(self), fields(user_id = %id))]
async fn get_user(&self, id: String) -> Result<User> {
    info!("Fetching user");

    match self.repo.get(&id).await {
        Ok(Some(user)) => {
            info!("User found");
            Ok(user)
        }
        Ok(None) => {
            warn!("User not found");
            Err(ServiceError::NotFound(format!("User {}", id)))
        }
        Err(e) => {
            error!("Database error: {}", e);
            Err(e.into())
        }
    }
}

Metrics

rust
use prometheus::{Counter, Histogram, Registry};

lazy_static! {
    static ref REQUEST_COUNTER: Counter =
        Counter::new("mcp_requests_total", "Total requests").unwrap();

    static ref REQUEST_DURATION: Histogram =
        Histogram::new("mcp_request_duration_seconds", "Request duration").unwrap();

    static ref ERROR_COUNTER: Counter =
        Counter::new("mcp_errors_total", "Total errors").unwrap();
}

async fn handle_request_with_metrics(req: Request) -> Result<Response> {
    REQUEST_COUNTER.inc();
    let _timer = REQUEST_DURATION.start_timer();

    match handle_request(req).await {
        Ok(resp) => Ok(resp),
        Err(e) => {
            ERROR_COUNTER.inc();
            Err(e)
        }
    }
}

Configuration Management

rust
use config::{Config, ConfigError, Environment, File};
use serde::Deserialize;

#[derive(Debug, Deserialize)]
struct AppConfig {
    server: ServerConfig,
    database: DatabaseConfig,
    cache: CacheConfig,
    logging: LoggingConfig,
}

#[derive(Debug, Deserialize)]
struct ServerConfig {
    host: String,
    port: u16,
    timeout_seconds: u64,
}

impl AppConfig {
    fn load() -> Result<Self, ConfigError> {
        Config::builder()
            .add_source(File::with_name("config/default"))
            .add_source(File::with_name("config/local").required(false))
            .add_source(Environment::with_prefix("APP"))
            .build()?
            .try_deserialize()
    }
}

Best Practices Checklist

Development

  • Use type-driven design
  • Implement proper error handling
  • Write comprehensive tests
  • Use async properly
  • Follow Rust idioms

Security

  • Validate all inputs
  • Use parameterized queries
  • Implement authentication
  • Add authorization checks
  • Audit dependencies

Performance

  • Use connection pooling
  • Implement caching
  • Optimize database queries
  • Use concurrent operations
  • Profile and benchmark

Operations

  • Add structured logging
  • Implement metrics
  • Create health checks
  • Handle graceful shutdown
  • Document deployment

Maintenance

  • Version your API
  • Write documentation
  • Create examples
  • Set up CI/CD
  • Monitor in production

Your Role

When reviewing or designing MCP servers:

  1. Assess Architecture: Is the design clean and maintainable?
  2. Check Security: Are inputs validated? Is auth implemented?
  3. Review Performance: Are operations optimized? Is caching used?
  4. Validate Testing: Are tests comprehensive? Is coverage good?
  5. Ensure Observability: Is logging/metrics in place?

Your goal is to help developers build production-ready MCP servers that are secure, performant, maintainable, and observable.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mcp Server Best Practices AI skill do?

Production-ready patterns and best practices for MCP servers - architecture, security, performance, and maintenance

Why use Mcp Server Best Practices on TypingMind?

Because you install it once and use it with any model. Mcp Server Best Practices is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mcp Server Best Practices in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jiaxiaojunQAQ/SkillJect/tree/main/data/skills_sample/mcp-best-practices. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mcp Server Best Practices?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mcp Server Best Practices?

As many as you like. As long as a model supports skills, you can use Mcp Server Best Practices with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mcp Server Best Practices AI skill free?

It is published on GitHub by jiaxiaojunQAQ. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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