Click on "Deploy 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., "@Netmex MCP list all available tools in my project"
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 lightweight and extendable Model Context Protocol (MCP) server toolkit for building custom tools that integrate with AI assistants.
This package provides a ready-to-use MCP server, a clean structure for defining tools, and the option for developers to add their own tools without modifying the package.
npm install @netmex/mcpyarn add @netmex/mcpRelated MCP server: SimpleMCP
After installing, you can run the MCP server using:
npx netmex-mcpTo add your own tools, create a tool file inside a mcp-tools/ directory in your project root:
Example Tool (TypeScript)
import type { Tool } from "@netmex/mcp/types";
const GreetTool: Tool = {
name: "greet",
description: "Returns a friendly greeting",
inputSchema: {
type: "object",
properties: {
name: { type: "string", description: "Name to greet" }
},
required: ["name"]
},
handler: async ({ name }) => ({
content: [
{
type: "text",
text: `Hello, ${name}!`
}
]
})
};
export default GreetTool;Once placed inside mcp-tools/, tools are automatically discovered by the loader and exposed to MCP clients.
No registration needed. No editing package code. Just drop the tool file in place.
Each tool receives structured arguments, validated according to its inputSchema.
your-project/
├── mcp-tools/
│ ├── GreetTool.ts
│ └── AnotherTool.ts
├── node_modules/
├── package.json
└── ...You can also install tools from npm. Any dependency whose name starts with:
netmex-mcp-tool-will be auto-loaded by the server.
Example:
npm install netmex-mcp-tool-analyticsIf a tool throws or returns an invalid value, the server automatically returns a JSON-RPC error:
{
"error": {
"code": -32603,
"message": "Internal error",
"data": { "details": "Something went wrong" }
}
}Use try/catch inside handlers for custom error responses.
This package includes a few tools by default:
about
hello
(more coming soon)
To build the MCP server locally:
npm install
npm run buildTo start it directly:
npm run startFor more information on the Model Context Protocol, visit the official MCP documentation.
This project is licensed under the MIT License.
Returns information about this MCP server
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe the format or structure of returned information, potential limitations, or error conditions. The description is accurate but lacks operational details that would help an agent understand what to expect from the response.
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 a single, efficient sentence that communicates the essential purpose without any wasted words. It's front-loaded with the core functionality and appropriately sized for a simple tool. Every word earns its place in this minimal yet complete statement.
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?
For a simple, parameterless tool with no output schema, the description provides adequate but minimal context. It tells what the tool does but doesn't describe the return format or content. Given the tool's simplicity, this is acceptable but leaves the agent guessing about the structure of the returned information.
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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description correctly indicates this is a parameterless operation that returns server information, which aligns perfectly with the empty input schema. No additional parameter information is needed or provided.
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 clearly states the tool's purpose with a specific verb ('Returns') and resource ('information about this MCP server'). It distinguishes from the sibling 'hello' tool by focusing on server metadata rather than a greeting function. However, it doesn't explicitly contrast with the sibling tool in the description text itself.
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 implies usage context (when you need server information) but provides no explicit guidance about when to use this versus alternatives. There's no mention of prerequisites, timing considerations, or comparison to the 'hello' sibling tool. The usage is reasonably inferable but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A simple greeting tool
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional name |
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. 'A simple greeting tool' reveals nothing about what the tool actually does behaviorally - whether it's read-only or has side effects, what permissions might be needed, what the output format is, or any rate limits. The description provides minimal behavioral context beyond the name.
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 three words. It's front-loaded with the core concept and contains zero wasted words. While it's under-specified, this dimension specifically evaluates conciseness and structure, not completeness.
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 (1 optional parameter, no output schema, no annotations), the description is incomplete. It doesn't explain what kind of greeting is produced, in what format, or what the tool actually does. For even a simple tool, 'A simple greeting tool' leaves too many questions unanswered about the tool's behavior and output.
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 schema has 100% description coverage with the single parameter 'name' documented as 'Optional name'. The description adds no parameter information beyond what's already in the schema. 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 'A simple greeting tool' is a tautology that essentially restates the tool name 'hello' without specifying what the tool actually does. It doesn't provide a specific verb+resource combination or distinguish this greeting tool from the sibling 'about' tool. The description is vague about whether it generates, displays, or sends a greeting.
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 when this greeting tool should be used, when it shouldn't be used, or how it differs from the sibling 'about' tool. The agent receives no contextual guidance for tool selection.
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.
abouthelloScored across 2 tools
The two tools have clearly distinct purposes: 'about' provides server metadata while 'hello' offers a simple greeting. There is no overlap in functionality, making it impossible to confuse them.
Both tools use lowercase, single-word names that are simple and descriptive. The naming pattern is perfectly consistent across the minimal toolset.
With only two tools—one for metadata and one for a greeting—the server feels extremely thin. This minimal set suggests either an incomplete implementation or a server with trivial functionality that doesn't justify its existence as an MCP service.
The server lacks any meaningful domain coverage. There are no tools for actual network management or monitoring (implied by 'Netmex'), making this surface severely incomplete for any practical purpose beyond demonstration.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
A registry of AI agent tools — MCP servers, APIs, CLIs, SDKs — kept current by automated ingestion.
MCP registry & directory: search, find & install 31k+ MCP servers & tools. Catalog and marketplace.
