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AdarshChaudhary03

MCP Server Deployment Demo

by AdarshChaudhary03
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

MCP Server Deployment Demo

This project is a demonstration of how to create and deploy a simple server that adheres to the Model-Context-Protocol (MCP). It provides a basic tool that can be called by an MCP client, such as an AI model like Claude.

Overview

The Model-Context-Protocol (MCP) is a specification for communication between a large language model (or other AI agent) and a local development environment. It allows the model to access local context and execute tools securely.

This project implements a simple MCP server using the mcp Python library. The server exposes a single tool, add, which takes two integers and returns their sum.

Related MCP server: MCP Server Basic

Features

  • A simple FastMCP server implementation.

  • A basic add(x: int, y: int) -> int tool.

  • Packaged as a Python project using pyproject.toml and setuptools.

  • Ready for installation and deployment.

Project Structure

  • pyproject.toml: Defines project metadata, dependencies, and build configuration. The [project.scripts] section defines the mcp-server command.

  • src/mcpserver/deployment.py: Contains the core logic for the MCP server, including the definition of the add tool.

  • src/mcpserver/__main__.py: Provides the main entry point to run the server from the command line.

Getting Started

Prerequisites

  • Python 3.12+

  • uv (a fast Python package installer and resolver)

Installation

  1. Clone the repository:

    git clone https://github.com/AdarshChaudhary03/mcp-server-deployment.git
cd mcp-server-deployment

  2. Install dependencies: Create a virtual environment and install the project in editable mode.

    uv venv
source .venv/bin/activate
uv pip install -e .

Running the Server

Once installed, you can run the MCP server using the script defined in pyproject.toml:

mcp-server

Claude Integration

To use this server as an MCP host for a client like Claude, you need to update your Claude configuration file (claude-config.json). Add the following entry to the mcpHosts section.

This configuration tells the Claude client how to launch and communicate with your MCP server. It uses uvx to run the server directly from the Git repository, ensuring you are always using the latest version.

Note: Make sure to replace the command and directory paths with the correct absolute paths for your system.

"MCPServer": {
  "command": "/Library/Frameworks/Python.framework/Versions/3.13/bin/uvx",
  "args": [
    "--directory",
    "/Users/adarshchaudhary/Desktop/ai-projects/mcp-servers/mcp-server-deployment",
    "--from",
    "git+https://github.com/AdarshChaudhary03/mcp-server-deployment",
    "mcp-server"
  ]
}

Available Tools

1 tool
addC

Adds two integers.

ParametersJSON Schema
NameRequiredDescriptionDefault
xYes
yYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.6/5.0
Behavior1/5

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. However, it only states the basic operation ('Adds two integers') and does not reveal any behavioral traits such as error handling, performance characteristics, side effects, or output format. This is inadequate for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded with a single, clear sentence: 'Adds two integers.' There is no wasted verbiage or unnecessary elaboration, making it efficient and easy to parse for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (simple integer addition) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks details on behavioral aspects and parameter semantics, which are important even for simple tools. The output schema mitigates some gaps, but overall completeness is limited.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description does not add any meaning beyond the schema; it merely restates the operation without explaining what 'x' and 'y' represent, their constraints, or examples. This fails to compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Adds two integers.' It specifies the verb ('Adds') and the resource/operation ('two integers'), making it unambiguous. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. It simply states what the tool does without any context about scenarios, prerequisites, or exclusions. This lack of usage instructions limits its helpfulness for an AI agent in decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool update
    • First observedadd
Install Server
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TDQS

C2.8/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool 'add' has a clear and distinct purpose that cannot be mistaken for any other tool in the set.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'add' follows a simple verb pattern, which is appropriate for its function.

Tool Count2/5

A single tool for a server named 'MCP Server Deployment Demo' is too few for the apparent scope, which suggests deployment-related operations. This minimal set feels thin and inadequate for handling deployment tasks beyond basic arithmetic.

Completeness1/5

The tool set is severely incomplete for a deployment domain. With only an 'add' tool for integers, there are obvious gaps in deployment operations such as deploying, configuring, monitoring, or managing servers, making the surface unusable for its stated purpose.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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