Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server Boilerplate show me how to add a new tool to my custom MCP server"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A starter template for building MCP (Model Context Protocol) servers. This boilerplate provides a clean foundation for creating your own MCP server that can integrate with Claude Desktop, Cursor, Claude Code, Gemini, and other MCP-compatible AI assistants.
This boilerplate helps you quickly start building:
Custom tools for AI assistants
Resource providers for dynamic content
Prompt templates for common operations
Integration points for external APIs and services
Related MCP server: MCP Server Boilerplate
Two example tools: "hello-world" and "get-mcp-docs"
TypeScript support with ES2022 target and ES modules
Multi-client installation scripts (Claude Desktop, Cursor, Claude Code, Gemini, etc.)
Automatic npm publishing workflow
Environment variable support via .env.local
Clean project structure with Zod validation
This MCP server template provides:
A basic server setup using the MCP SDK
Example tool implementation
Build and installation scripts
TypeScript configuration for development
The included example demonstrates how to create a simple tool that takes a name parameter and returns a greeting.
You can use this MCP server directly without cloning:
# Run the server directly with npx
npx @r-mcp/boilerplate# Clone the boilerplate
git clone <your-repo-url>
cd mcp-server-boilerplate
# Install dependencies
pnpm install
# Build the project
pnpm run build
# Start the server
pnpm startThis boilerplate includes convenient installation scripts for different MCP clients:
# Install to all MCP clients (Claude Desktop, Cursor, Claude Code, Gemini, MCP)
pnpm run install-server
# Install to specific clients
pnpm run install-desktop # Claude Desktop
pnpm run install-cursor # Cursor IDE
pnpm run install-code # Claude Code CLI
pnpm run install-code-library # Claude Code Library (~/.claude/mcp-library/)
pnpm run install-mcp # Local .mcp.json for development
# You can also combine multiple targets:
node scripts/update-config.js cursor code desktopThese scripts will:
Build the project automatically (TypeScript compilation + chmod permissions)
Configure clients to use npx @r-mcp/<directory-name>@latest (auto-updating)
Only the local .mcp.json uses the development version (node dist/index.js)
Include environment variables from .env.local if present
To publish your customized MCP server:
# Build, commit, and publish to npm in one command
pnpm run releaseThis script (scripts/build-and-publish.js) will:
Commit any pending changes first
Update package name to @r-mcp/<directory-name>
Update bin name to match directory
Increment patch version automatically
Build the TypeScript project
Commit version bump to git
Push to remote repository
Publish to npm with public access
The installation scripts automatically configure your MCP clients. For reference, here's what gets added:
{
"mcpServers": {
"boilerplate": {
"command": "npx",
"args": ["-y", "@r-mcp/boilerplate@latest"],
"env": {
// Environment variables from .env.local are included here
}
}
}
}.mcp.json):{
"mcpServers": {
"boilerplate": {
"command": "node",
"args": ["/absolute/path/to/dist/index.js"],
"env": {
// Environment variables from .env.local are included here
}
}
}
}After running installation scripts, restart your MCP client to connect to the server.
Tools are functions that the AI assistant can call. Here's the basic structure:
server.tool(
"tool-name",
"Description of what the tool does",
{
// Zod schema for parameters
param1: z.string().describe("Description of parameter"),
param2: z.number().optional().describe("Optional parameter"),
},
async ({ param1, param2 }) => {
// Your tool logic here
return {
content: [
{
type: "text",
text: "Your response",
},
],
};
}
);Resources provide dynamic content that the AI can access:
server.resource(
"resource://example/{id}",
"Description of the resource",
async (uri) => {
// Extract parameters from URI
const id = uri.path.split("/").pop();
return {
contents: [
{
uri,
mimeType: "text/plain",
text: `Content for ${id}`,
},
],
};
}
);Prompts are reusable templates:
server.prompt(
"prompt-name",
"Description of the prompt",
{
// Parameters for the prompt
topic: z.string().describe("The topic to discuss"),
},
async ({ topic }) => {
return {
description: `A prompt about ${topic}`,
messages: [
{
role: "user",
content: {
type: "text",
text: `Please help me with ${topic}`,
},
},
],
};
}
);├── src/
│ └── index.ts # Main MCP server implementation
├── scripts/
│ ├── update-config.js # Multi-client configuration installer
│ └── build-and-publish.js # Automated npm publishing workflow
├── dist/ # Compiled JavaScript (generated)
├── package.json # Project configuration
├── tsconfig.json # TypeScript configuration
├── CLAUDE.md # Claude Code instructions
├── .env.local # Environment variables (optional)
└── README.md # This fileMake changes to src/index.ts
Run pnpm run build to compile TypeScript
Test your server with pnpm start
Use pnpm run install-mcp for local testing
Restart your MCP client to load changes
Test your changes locally
Run pnpm run release to publish to npm
Clients using npx @r-mcp/<your-package>@latest auto-update
No client reconfiguration needed
Create a .env.local file for environment-specific configuration:
# .env.local
API_KEY=your-api-key
DATABASE_URL=your-database-urlThese variables are automatically included in MCP server configurations during installation.
Fork or clone this boilerplate
Customize the server name and tools in src/index.ts
Add your own tools, resources, and prompts
Configure environment variables in .env.local
Run pnpm run release to publish your server
Install to clients with pnpm run install-server
MIT
Make an MCP server
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The name of the MCP server |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. 'Make an MCP server' suggests a creation/write operation but provides no information about what gets created, whether authentication is required, what the output looks like, or any side effects. The description offers zero behavioral context beyond the vague action implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just three words, this is under-specification rather than effective conciseness. The description doesn't front-load important information and fails to communicate the tool's purpose effectively. Every word should earn its place, but here the words don't provide sufficient value to justify their inclusion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and a vague description, the description is completely inadequate. For a tool that appears to perform some kind of creation/retrieval operation (based on the name 'get-mcp-docs' and description 'Make an MCP server'), the description fails to explain what the tool actually does, what it returns, or how to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'name' clearly documented as 'The name of the MCP server.' The description doesn't add any meaningful parameter semantics beyond what the schema already provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Make an MCP server' is a tautology that essentially restates the tool name 'get-mcp-docs' in different words. It doesn't clearly specify what the tool actually does - whether it creates, retrieves, or generates documentation for MCP servers. The description lacks a specific verb-resource combination and doesn't distinguish from the sibling 'hello-world' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance on when to use this tool versus alternatives. There's no mention of context, prerequisites, or comparison to the sibling 'hello-world' tool. The agent receives no information about appropriate use cases or when this tool would be the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Say hello to the user
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The name of the user |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Say hello to the user' implies a simple output operation, but it doesn't describe any behavioral traits such as whether it's read-only, if it has side effects, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just four words ('Say hello to the user'), front-loaded with the core purpose. There's zero wasted language, making it efficient and easy to parse, though this conciseness comes at the cost of completeness in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns (e.g., a greeting string), any constraints on the 'name' parameter, or behavioral context. While the tool is basic, the description lacks sufficient detail for an agent to fully understand its operation beyond the minimal schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'name' documented as 'The name of the user'. The description doesn't add any meaning beyond this schema information, such as explaining how the name is used in the greeting. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Say hello to the user' clearly states the tool's purpose with a specific verb ('say hello') and target ('to the user'). It's not tautological with the name 'hello-world', but it doesn't differentiate from potential sibling tools like 'get-mcp-docs', which serves a completely different purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of context, prerequisites, or comparison with the sibling tool 'get-mcp-docs'. It simply states what the tool does without indicating appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get-mcp-docshello-worldThe two tools have completely distinct purposes: 'get-mcp-docs' is for creating MCP servers, while 'hello-world' is for greeting users. There is no overlap or ambiguity between these functions, making tool selection straightforward.
The naming is inconsistent: 'get-mcp-docs' uses kebab-case with a verb-object structure, while 'hello-world' uses kebab-case but is a noun-phrase without a clear verb. This mixed convention lacks a predictable pattern, though both use kebab-case.
With only 2 tools, the server feels under-scoped for a 'boilerplate' purpose, which typically implies a foundational set of utilities. This minimal count may not provide sufficient coverage for common MCP development tasks, making it too thin for its apparent scope.
Given the server name 'MCP Server Boilerplate', there are significant gaps in coverage for typical boilerplate functions, such as setup, configuration, testing, or deployment tools. The two tools provided (documentation and greeting) do not form a complete surface for MCP server development, leading to potential agent failures in broader tasks.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server for building and testing AI agents with multi-model experimentation and insights.
A simple Typescript MCP server built using the official MCP Typescript SDK and smithery/cli. This…
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/CaptainCrouton89/mcp-boilerplate'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
