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Create Mcp Servers

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glittercowboy
create-mcp-servers

Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.

Overview

Publisherglittercowboy
Repositorytaches-cc-resources
Skill namecreate-mcp-servers
Stars
2K
Forks
411
Bundled files
22
LicenseMIT
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.

  • 22 bundled files

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

  • Open source

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

Installation

Install the Create Mcp Servers 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/glittercowboy/taches-cc-resources.git /tmp/taches-cc-resources
mkdir -p .claude/skills
cp -r /tmp/taches-cc-resources/skills/create-mcp-servers .claude/skills/create-mcp-servers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Mcp Servers 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 Create Mcp Servers 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 Create Mcp Servers 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.

<essential_principles>

<the_5_rules> Every MCP server must follow these:

  1. Never Hardcode Secrets - Use ${VAR} expansion in configs, environment variables in code
  2. Use cwd Property - Isolates dependencies (not --cwd in args)
  3. Always Absolute Paths - which uv to find paths, never relative
  4. One Server Per Directory - ~/Developer/mcp/{server-name}/
  5. Use uv for Python - Better than pip, handles venvs automatically </the_5_rules>

<security_checklist>

  • Never ask user to paste secrets into chat
  • Always use environment variables for credentials
  • Use ${VAR} expansion in configs
  • Provide exact commands for user to run in terminal
  • Verify environment variable existence without showing values
  • Never hardcode API keys in code or configs </security_checklist>

<architecture_decision> Operation count determines architecture:

  • 1-2 operations → Traditional pattern (flat tools)
  • 3+ operations → On-demand discovery pattern (meta-tools)

Traditional: Each operation is a separate tool On-demand: 4 meta-tools (discover, get_schema, execute, continue) + operations.json </architecture_decision>

Standard location: ~/Developer/mcp/{server-name}/

</essential_principles>

No context provided (skill invoked without description): Use AskUserQuestion:

  • header: "Mode"
  • question: "What would you like to do?"
  • options:
    • "Create a new MCP server" → workflows/create-new-server.md
    • "Update an existing MCP server" → workflows/update-existing-server.md
    • "Troubleshoot a server" → workflows/troubleshoot-server.md

Context provided (user described what they want): Route directly to workflows/create-new-server.md

<workflows_index>

WorkflowPurpose
create-new-server.mdFull 8-step workflow from intake to verification
update-existing-server.mdModify or extend an existing server
troubleshoot-server.mdDiagnose and fix connection/runtime issues
</workflows_index>

<templates_index>

TemplatePurpose
python-server.pyTraditional pattern starter for Python
typescript-server.tsTraditional pattern starter for TypeScript
operations.jsonOn-demand discovery operations definition
</templates_index>

<scripts_index>

ScriptPurpose
setup-python-project.shInitialize Python MCP project with uv
setup-typescript-project.shInitialize TypeScript MCP project with npm
</scripts_index>

<references_index> Core workflow:

  • creation-workflow.md - Complete step-by-step with exact commands

Architecture patterns:

  • traditional-pattern.md - For 1-2 operations (flat tools)
  • large-api-pattern.md - For 3+ operations (on-demand discovery)

Language-specific:

  • python-implementation.md - Async patterns, type hints
  • typescript-implementation.md - Type safety, SDK features

Advanced topics:

  • oauth-implementation.md - OAuth with stdio isolation
  • response-optimization.md - Field truncation, pagination
  • tools-and-resources.md - Resources API, prompts, streaming
  • testing-and-deployment.md - Unit tests, packaging, publishing
  • validation-checkpoints.md - All validation checks
  • adaptive-questioning-guide.md - Question templates for intake
  • api-research-template.md - API research document format </references_index>

<quick_reference>

bash
# List servers
claude mcp list

# Add server (Python)
claude mcp add --transport stdio <name> \
  --env API_KEY='${API_KEY}' \
  -- uv --directory ~/Developer/mcp/<name> run python -m src.server

# Add server (TypeScript)
claude mcp add --transport stdio <name> \
  --env API_KEY='${API_KEY}' \
  -- node ~/Developer/mcp/<name>/build/index.js

# Remove server
claude mcp remove <name>

# Check logs
tail -f ~/Library/Logs/Claude/mcp-server-<name>.log

# Find paths
which uv && which node && which python

</quick_reference>

<troubleshooting_quick> Server not appearing: Check claude mcp list, verify config in ~/.claude/settings.json

"command not found": Use absolute paths from which uv / which node

Environment variable not found:

bash
echo $MY_API_KEY  # Check if set
echo 'export MY_API_KEY="value"' >> ~/.zshrc && source ~/.zshrc

Secrets visible in conversation: STOP. Delete conversation. Rotate credentials. Never paste secrets in chat.

Full troubleshooting: workflows/troubleshoot-server.md </troubleshooting_quick>

<success_criteria> A production-ready MCP server has:

  • Valid configuration in Claude Code (claude mcp list shows ✓ Connected)
  • Valid configuration in Claude Desktop config
  • Environment variables set securely in ~/.zshrc
  • Architecture matches operation count
  • OAuth stdio isolation if applicable
  • Response optimization for list/search operations
  • All validation checkpoints passed
  • No errors in logs </success_criteria>

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 Create Mcp Servers AI skill do?

Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.

Why use Create Mcp Servers on TypingMind?

Because you install it once and use it with any model. Create Mcp Servers 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 Create Mcp Servers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glittercowboy/taches-cc-resources/tree/main/skills/create-mcp-servers. 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 Create Mcp Servers?

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 Create Mcp Servers?

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

Is the Create Mcp Servers AI skill free?

Yes. It is published on GitHub by glittercowboy under the MIT license. 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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