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mcp-developmentMCP 开发

Agent Skill

mcp-development 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

974

周安装

41

GitHub Stars

4

下载量

341
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mcp-development(MCP 开发)
来源仓库:https://github.com/eyadsibai/ltk
仓库路径:skills/mcp-development
安装命令:
npx skills add https://github.com/eyadsibai/ltk --skill mcp-development
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/eyadsibai/ltk --skill mcp-development

简介

用于构建 MCP(Model Context Protocol)服务器,连接 LLM 与外部服务。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中设计工具型工作流而非简单 API 封装。
  • 强调以任务为中心、返回高信号数据与错误可操作化。
  • 安装命令:npx skills add https://github.com/eyadsibai/ltk --skill mcp-development。
  • 使用前请规划工具边界与上下文隔离机制。

SKILL.md

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

PrincipleWhy
Consolidate operationsSingle tool for complete tasks
Return high-signal dataAgents have limited context
Provide format options"concise" vs "detailed" modes
Use human-readable IDsNot technical codes
Make errors actionableGuide 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

StepAction
Study MCP ProtocolRead modelcontextprotocol.io/llms-full.txt
Study SDK docsPython or TypeScript SDK README
Study target APIRead ALL available documentation
Create implementation planBefore writing code

Phase 2: Design

DecisionOptions
LanguagePython (FastMCP) or TypeScript
Tool granularityAtomic vs workflow-oriented
Response formatJSON, Markdown, or both
Error handlingWhat errors can occur, how to recover

Phase 3: Implementation

ComponentPurpose
Input validationPydantic (Python) or Zod (TypeScript)
Tool descriptionsClear, with examples
Error messagesInclude suggested next steps
Response formattingConsistent across tools

Phase 4: Testing

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

ApproachHow
Evaluation harnessRecommended
tmux sessionRun server separately
Timeout wrappertimeout 5s python server.py
MCP InspectorOfficial debugging tool

Tool Annotations

AnnotationMeaningDefault
readOnlyHintDoesn't modify statefalse
destructiveHintCan cause damagetrue
idempotentHintRepeated calls safefalse
openWorldHintInteracts externallytrue

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


Input Design

Validation Patterns

PatternUse Case
Required fieldsCore parameters
Optional with defaultsConvenience parameters
EnumsLimited valid values
Min/max constraintsNumeric bounds
Pattern matchingFormat validation (email, URL)

Parameter Naming

GoodBadWhy
user_emaileSelf-documenting
limitmax_results_to_returnConcise but clear
include_archivediaDescriptive boolean

Response Design

Format Options

FormatUse Case
JSONProgrammatic use, structured data
MarkdownHuman readability, reports
HybridJSON in markdown code blocks

Response Guidelines

GuidelineWhy
~25,000 token limitContext constraints
Truncate with indicatorDon't silently cut
Support paginationlimit and offset params
Include metadataTotal count, has_more

Error Handling

Error Message Structure

ElementPurpose
What failedClear description
Why it failedRoot cause if known
How to fixSuggested next action
ExampleCorrect usage

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


Quality Checklist

Code Quality

CheckDescription
No duplicated codeExtract shared logic
Consistent formatsSimilar ops return similar structure
Full error handlingAll external calls wrapped
Type coverageAll inputs/outputs typed
Comprehensive docstringsEvery tool documented

Tool Quality

CheckDescription
Clear descriptionsModel knows when to use
Good examplesIn docstring
Sensible defaultsReduce required params
Consistent namingGroup related with prefixes

Best Practices

PracticeWhy
One tool = one purposeClear mental model
Comprehensive descriptionsLLM selection accuracy
Include examples in docstringsShow expected usage
Return actionable errorsEnable self-correction
Test with actual LLMReal-world validation
Version your serverTrack compatibility

Resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.36%
按下载量换算117

Claude

29.63%
按下载量换算101

Cursor

17.97%
按下载量换算61

Gemini CLI

8.8%
按下载量换算30

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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