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 that fetches weather data"
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, Cursor, or 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: JSON MCP Boilerplate
Simple "hello-world" tool example
TypeScript support with proper type definitions
Easy installation scripts for different MCP clients
Clean project structure ready for customization
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.
# 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:
# For Claude Desktop
pnpm run install-desktop
# For Cursor
pnpm run install-cursor
# For Claude Code
pnpm run install-code
# Generic installation
pnpm run install-serverThese scripts will build the project and automatically update the appropriate configuration files.
The installation script will automatically add the configuration, but you can also manually add it to your claude_desktop_config.json file:
{
"mcpServers": {
"your-server-name": {
"command": "node",
"args": ["/path/to/your/dist/index.js"]
}
}
}Then restart Claude Desktop 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 server implementation
├── scripts/ # Installation and utility scripts
├── dist/ # Compiled JavaScript (generated)
├── package.json # Project configuration
├── tsconfig.json # TypeScript configuration
└── README.md # This fileMake changes to src/index.ts
Run pnpm run build to compile
Test your server with pnpm start
Use the installation scripts to update your MCP client configuration
Update package.json with your project details
Customize the server name and tools in src/index.ts
Add your own tools, resources, and prompts
Integrate with external APIs or databases as needed
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' doesn't indicate whether this is a read or write operation, what permissions might be required, whether it has side effects, what happens when invoked, or what kind of output to expect. The description provides zero behavioral context beyond the ambiguous verb 'Make'.
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 the description is extremely concise (just three words), this represents under-specification rather than effective brevity. The single phrase 'Make an MCP server' doesn't provide enough information to be useful, making it inefficient rather than appropriately concise. Every word should earn its place, but here the words don't provide sufficient value.
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 that this is a tool with one parameter but no annotations and no output schema, the description is completely inadequate. For a tool that presumably performs some action (implied by 'Make'), the description should explain what happens when invoked, what the result looks like, and any important behavioral characteristics. The current description leaves the agent guessing about the tool's purpose and behavior.
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 'name' parameter clearly documented as 'The name of the MCP server'. The description adds no additional parameter information beyond what the schema 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, which applies here.
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' without clarifying what 'make' means in this context. It doesn't specify what resource is being created or what the tool actually does - whether it generates documentation, creates a server instance, or something else. The description fails to distinguish this tool from its sibling 'hello-world' or explain what specific action is performed.
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 prerequisites, appropriate contexts, or when this tool should be selected over the sibling 'hello-world' tool. The agent receives no usage instructions beyond the vague description text.
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 read-only, non-destructive action, but it doesn't specify output format, side effects, or any constraints like rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 'Say hello to the user' is a single, clear sentence with zero wasted words. It is appropriately sized for a simple tool and front-loads the core purpose effectively.
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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on output, usage context, or behavioral traits. Without an output schema, the description should ideally explain return values, but for such a simple tool, it meets the bare minimum.
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 adds no additional parameter information beyond what the schema provides. According to the rules, when schema_description_coverage is high (>80%), the baseline score 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 'Say hello to the user' clearly states the tool's purpose with a specific verb ('say hello') and target ('to the user'). It distinguishes from the sibling tool 'get-mcp-docs' which presumably retrieves documentation. However, it doesn't specify the exact output format or mechanism, keeping it at a 4 rather than a perfect 5.
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 is no mention of context, prerequisites, or comparison to 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-docs2 fields changedInput schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falsehello-world2 fields changedInput schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falseget-mcp-docshello-worldThe two tools have completely distinct purposes: 'get-mcp-docs' is for server creation/documentation, while 'hello-world' is for user interaction. 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 lacks a clear verb and is more phrase-like. This mixed convention reduces predictability and readability across the tool set.
With only 2 tools, this server feels too thin for a 'boilerplate' purpose, which typically implies a foundational or example set. The count is insufficient to demonstrate meaningful coverage or utility, making it borderline inadequate for its apparent scope.
For a boilerplate server, there are significant gaps: it lacks core operations like setup, configuration, testing, or deployment tools. The two tools provided do not form a coherent or complete surface for server development, leaving obvious dead ends for agents.
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