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 Boilerplate Server add 15 and 27"
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 Model Context Protocol (MCP) server built with FastMCP that provides tools, resources, and prompts for greeting, math operations, user management, and system monitoring.
Tools: Greeting, math operations (add/multiply), user information lookup
Resources: Server info, user data, configuration settings
Prompts: User analysis, report generation, system health checks, troubleshooting
Related MCP server: ddg--mcp5
Python 3.12 or higher
uv (recommended) or pip
# Install with uv (recommended)
uv add mcp-server-boilerplate
# Or install with pip
pip install mcp-server-boilerplate # not published , this is boilerplateAfter installation, you can run the server directly:
mcp-server-boilerplateClone the repository:
git clone <YOUR_GITHUB_REPO_URL>
cd mcp_serverInstall uv (if not already installed):
# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Install dependencies:
uv syncAlternative with pip:
pip install fastmcp>=2.11.3Test the server:
uv run python main.pyYou should see the FastMCP banner and server startup message. Press Ctrl+C to stop.
greet(name: str) - Greet a person by name
add(a: int, b: int) - Add two numbers together
multiply(a: int, b: int) - Multiply two numbers together
get_user_info(user_id: int) - Get user information by ID
To use this MCP server with Claude Desktop, you need to add it to your configuration file.
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
First, find the required paths for your system:
# Find uv installation path
which uv
# Get your current directory
pwdAdd this to your claude_desktop_config.json (create the file if it doesn't exist):
{
"mcpServers": {
"mcp-server-boilerplate": {
"command": "mcp-server-boilerplate"
}
}
}{
"mcpServers": {
"mcp-server-boilerplate": {
"command": "/path/to/uv",
"args": [
"run",
"python",
"main.py"
],
"cwd": "/path/to/your/mcp_server"
}
}
}Important: Replace the paths with your actual values:
Replace /path/to/uv with the output from which uv
Replace /path/to/your/mcp_server with your project directory path
After adding the configuration, restart Claude Desktop to load the MCP server.
To test your server with MCP Inspector:
Start the server in one terminal:
uv run python main.pyIn MCP Inspector, use these connection settings:
Connection Type: Local Command
Command: /home/shaza/.local/bin/uv (use which uv to find your path)
Arguments: run python main.py
Working Directory: /home/shaza/shaza/mcp_server
Or alternatively, if using direct python:
Command: python3
Arguments: main.py
Working Directory: /home/shaza/shaza/mcp_server
1. "ModuleNotFoundError: No module named 'fastmcp'"
# Install dependencies
uv sync
# or
pip install fastmcp>=2.11.32. "python: command not found"
Use python3 instead of python
Make sure Python 3.12+ is installed
3. "uv: command not found"
Install uv following the installation instructions above
Or use the direct Python configuration in Claude Desktop
4. Claude Desktop connection issues
Verify all paths in your config are absolute (not relative)
Restart Claude Desktop after config changes
Check that the config file is valid JSON (no trailing commas)
Test the server runs manually first: uv run python main.py
5. MCP Inspector connection errors
Make sure the server isn't already running in another terminal
Use absolute paths for command and working directory
Verify uv is in your PATH
Test the server manually: uv run python main.py
Check your paths with which uv and pwd
Validate your JSON config at jsonlint.com
Check Claude Desktop logs for error messages
main.py - Entry point that starts the MCP server
server.py - Main server code with tools, resources, and prompts
pyproject.toml - Python project configuration and dependencies
Tools: Add functions decorated with @mcp.tool in server.py
Resources: Add functions decorated with @mcp.resource("uri")
Prompts: Add functions decorated with @mcp.prompt
All functions should include proper type hints and docstrings for the best experience.
Install build tools:
uv add --dev build twineCreate PyPI account at pypi.org
Configure authentication:
# Create API token at https://pypi.org/manage/account/token/
# Store it securely - you'll use it as password with username '__token__'Update version in pyproject.toml and mcp_server_boilerplate/__init__.py
Build the package:
uv run python -m buildTest upload to TestPyPI (recommended first time):
uv run twine upload --repository testpypi dist/*Upload to PyPI:
uv run twine upload dist/*Update version numbers in:
pyproject.toml
mcp_server_boilerplate/__init__.py
Clean previous builds:
rm -rf dist/ build/ *.egg-info/Build and upload:
uv run python -m build
uv run twine upload dist/*Update GitHub URLs in pyproject.toml
Test the package locally: uv run mcp-server-boilerplate
Add your email to pyproject.toml authors section
Ensure all sensitive information is removed
Add two numbers together
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. While 'Add two numbers together' implies a simple computation, it doesn't address potential issues like integer overflow, error handling, or whether this is a pure function. The description is minimal and lacks behavioral context beyond the basic operation.
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 with a single, clear sentence that directly states the tool's function. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded and easy to parse.
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 (two integer parameters, has output schema), the description is minimally adequate but lacks depth. With no annotations and an output schema present, the description doesn't need to explain return values, but it misses opportunities to clarify behavioral aspects or usage guidelines that would help an agent 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 description adds meaningful context beyond the input schema, which has 0% description coverage. By specifying 'two numbers together,' it clarifies that both parameters are numeric operands for addition, compensating for the schema's lack of parameter descriptions. However, it doesn't detail the specific roles of 'a' and 'b' beyond being numbers to add.
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 'Add two numbers together' clearly states the tool's purpose with a specific verb ('Add') and resource ('two numbers'), making it immediately understandable. However, it doesn't explicitly distinguish this from the 'multiply' sibling tool, which performs a different mathematical operation on the same type of inputs.
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 like 'multiply' for mathematical operations. It doesn't mention any prerequisites, constraints, or typical use cases, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get user information by ID
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it's a read operation ('Get'), implying non-destructive, but doesn't disclose permissions, rate limits, error handling, or return format. For a tool with no annotations, this leaves significant behavioral gaps.
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 with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.
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 (1 parameter) and the presence of an output schema, the description is minimally adequate. However, with no annotations and 0% schema coverage, it lacks behavioral and parametric details that would enhance completeness for agent use.
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?
Schema description coverage is 0%, but the description adds meaning by specifying the parameter is 'by ID'. However, it doesn't detail the ID format, constraints, or examples. With 1 parameter, the baseline is 4, but the minimal addition slightly compensates for the schema gap.
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 verb ('Get') and resource ('user information'), specifying it's by ID. It doesn't distinguish from siblings, but none are related tools (add, greet, multiply), so differentiation isn't needed. It's specific enough to understand the core function.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions. With unrelated siblings, explicit alternatives aren't needed, but general usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Greet a person by name
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. It states the action ('Greet') but doesn't describe what the tool actually does behaviorally—e.g., whether it returns a message, logs the greeting, or has side effects. 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 and front-loaded with a single, clear sentence: 'Greet a person by name'. There is no wasted text, making it efficient and easy to parse, which is ideal for a simple tool.
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 (1 parameter) and the presence of an output schema, the description is somewhat complete but lacks depth. It doesn't explain behavioral aspects or usage context, which are needed since annotations are absent. The output schema may cover return values, but the description should still provide more context for effective use.
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 1 parameter with 0% description coverage, so the schema provides no semantic details. The description adds minimal value by implying the parameter is a 'name' for greeting, but it doesn't specify format, constraints, or examples. Baseline is 3 since the schema coverage is low, but the description doesn't fully compensate for the lack of schema details.
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 ('Greet') and resource ('a person by name'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'add' or 'multiply', which have completely different functions, so it doesn't need sibling differentiation but could be more specific about what 'greet' entails.
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. It doesn't mention any context, prerequisites, or exclusions, such as whether it's for formal or informal greetings, or if it should be used in specific scenarios. This leaves the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Multiply two numbers together
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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. The description only states what the tool does (multiplication) but doesn't disclose any behavioral traits like error handling, performance characteristics, mathematical precision, or what happens with large numbers. For a mathematical operation tool with zero annotation coverage, this is a significant gap.
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 perfectly concise: 'Multiply two numbers together' - a single sentence with zero waste. It's front-loaded with the core functionality and appropriately sized for this simple mathematical operation. Every word earns its place.
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 this is a simple mathematical tool with an output schema (which presumably describes the result), the description is reasonably complete for the core functionality. However, with no annotations and 0% schema description coverage, it lacks behavioral context that would be helpful for an agent. The existence of an output schema means the description doesn't need to explain return values.
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 description coverage is 0%, so the description must compensate. The description mentions 'two numbers' which maps to the two parameters, but doesn't add meaning beyond what's obvious from the parameter names 'a' and 'b'. It doesn't explain that these are integers (from schema), provide examples, or discuss edge cases. With 0% schema coverage, the description adds minimal value beyond the obvious.
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: 'Multiply two numbers together' - a specific verb ('multiply') and resource ('two numbers'). It distinguishes from siblings like 'add' by specifying multiplication rather than addition. However, it doesn't explicitly mention the integer type restriction from the schema.
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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention sibling tools like 'add' for different mathematical operations, nor does it provide context about when multiplication is appropriate versus other operations. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The tools have unclear boundaries and overlapping purposes. 'add' and 'multiply' are distinct mathematical operations, but 'get_user_info' and 'greet' are unrelated to them and to each other, creating a confusing mix of domains. An agent might struggle to choose between tools for different tasks due to the lack of a cohesive theme.
The naming is inconsistent with mixed conventions. 'add' and 'multiply' use simple verb forms, while 'get_user_info' follows a verb_noun pattern and 'greet' is a standalone verb. This lack of a predictable pattern makes the tool set harder to navigate and understand.
With 4 tools, the count is borderline appropriate. It feels thin for a general-purpose server, as it lacks depth in any single domain (e.g., math or user management). However, it's not extreme, so it's reasonable but could benefit from more focused scope.
There are significant gaps in the tool surface for any inferred domain. If the domain is math, tools like subtract or divide are missing; if it's user management, tools for creating or updating users are absent. The set is incomplete, leading to potential agent failures when trying to perform common operations.
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A simple MCP server built with FastMCP and python
Primarily to be used as a template repository for developing MCP servers with FastMCP in Python, P…
A very simple remote MCP server that greets you, with a custom icon.
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