Provides a tool to send text prompts to configured n8n workflows and retrieve the results, enabling the AI to interact with automated external workflows.
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 All-in-One Server Turn this webinar transcript into a blog post"
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.
This is a versatile MCP server that demonstrates multiple capabilities of the Model Context Protocol (MCP). It provides tools for calculation, external integrations (webhooks), resources for context, and prompt templates.
Functions that the AI can execute:
Calculator: Basic arithmetic operations (add, subtract, multiply, divide).
Webhook Integration: send_to_webhook - Sends a text prompt to a configured n8n workflow and returns the response.
Contextual data the AI can read:
support://playbook: A full Customer Support Playbook containing company overview, tone rules, product details, and standard operating procedures.
Templates to structure AI interactions:
webinar_to_blog: A structured prompt template to convert webinar transcripts into engaging blog posts.
Related MCP server: mcp-web-calc
Python 3.10+
uv: A fast Python package installer and resolver.
Windows: powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh
Node.js & npm (optional, for the MCP Inspector)
Open your terminal in this directory (calculator_server).
Initialize the project (if you haven't already):
uv initAdd the required dependencies:
uv add "mcp[cli]" httpxYou can run the server directly using uv:
uv run server.pyNote: This will use stdio for communication, so it will expect input from stdin and won't show much output in the console unless connected to an MCP client.
To test the server using the MCP Inspector (a web-based tool to interact with your server):
npx @modelcontextprotocol/inspector uv run server.pyThis will launch the inspector in your browser. You can:
Tools: Test the calculator and webhook tools.
Resources: View the content of support://playbook.
Prompts: Execute the webinar_to_blog template.
To use this server with Claude for Desktop:
Open your Claude Desktop configuration file:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Add the server configuration. You can copy the content from claude_desktop_config.json in this directory:
{
"mcpServers": {
"calculator": {
"command": "uv",
"args": [
"--directory",
"C:\\Users\\Your_Name\\Desktop\\mcps\\calculator_server",
"run",
"server.py"
]
}
}
}Important: Make sure the path in --directory matches the actual absolute path to your calculator_server folder.
Restart Claude for Desktop.
Add two numbers
| 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. It discloses that the tool adds numbers, which is sufficient for a simple operation, but adds no extra context about edge cases or side effects.
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 three words, front-loaded, and every word is necessary. No wasted text.
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 tool with an output schema, the description is complete enough. It could include minor usage notes, but the low complexity makes it adequate.
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% and the description does not mention parameters. The parameter names 'a' and 'b' are somewhat self-explanatory, but the description adds no meaning beyond the schema.
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' uses a specific verb and resource, clearly stating the tool's function. It is distinct from siblings like subtract, multiply, and divide.
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 explicit guidance on when to use this tool versus alternatives is provided. The description implies use for addition, but lacks exclusions or alternative mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Divide a by b
| 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 and the description does not disclose edge cases like division by zero or error handling, which is a gap for a simple but critical 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?
Single sentence, front-loaded, no wasted words.
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 simplicity and presence of output schema, the description is nearly complete except for missing edge case handling.
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 parameters are self-explanatory ('a' and 'b' as numbers), but the description adds no additional meaning beyond the schema.
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 'Divide a by b' uses a specific verb and resource, clearly distinguishing it from siblings like add, multiply, and subtract.
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 explicit guidance on when to use this tool vs alternatives, but the context of arithmetic operations makes it implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Multiply two numbers
| 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?
Without annotations, the description carries full burden. It correctly states the operation but lacks disclosure of edge cases like overflow, precision, or handling of special values such as NaN or Infinity. For a simple tool, this is minimally acceptable.
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 three words, no filler, and front-loaded information. 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 the simplicity (2 required number parameters, no nested objects, output schema present), the description is adequate but not complete. It omits return value details and edge cases, though the output schema exists to partially compensate.
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 coverage is 0%, but the description adds no meaning beyond the schema's 'a' and 'b' as numbers. It does not explain their roles or relationships, leaving the agent to infer from parameter names alone.
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 explicitly states the verb 'Multiply' and the resource 'two numbers', clearly indicating the tool's purpose. It distinguishes well from sibling tools like add, subtract, and divide.
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 multiply versus the sibling arithmetic tools. The description does not mention best practices, prerequisites, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send a prompt to the n8n webhook and get the response.
Args: prompt: The text prompt to process
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose side effects, auth needs, or error behavior, but only says 'send and get response', lacking 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 very short and to the point, but could include more detail without being verbose.
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 tool, the description covers the basic purpose but lacks details about the webhook, response format, or error handling, leaving gaps.
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 meaning to the 'prompt' parameter (text prompt to process) beyond the schema's type-only definition, but coverage is minimal.
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 sends a prompt to an n8n webhook and returns a response, distinguishing it from the arithmetic sibling tools.
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 on when to use this tool, no mention of alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Subtract b from a
| 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, the description must convey behavioral traits. It only states the basic operation, omitting details like return value type, error handling, or edge cases. The presence of an output schema is not leveraged in the description.
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, front-loaded sentence with no wasted words. It conveys the core functionality efficiently.
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 and the presence of an output schema, the description adequately defines the purpose. However, for a tool with no annotations, a bit more context on return or behavior would improve completeness.
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%, and the description does not add parameter-specific meaning beyond the names and types. It confirms order but fails to provide constraints or additional context that would compensate for the lack of schema descriptions.
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 'Subtract b from a' is a specific verb+resource statement that clearly defines the operation and order. It distinguishes the tool from siblings like add, divide, and multiply.
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 explicit usage guidelines are provided, but for a simple arithmetic tool, the context of use is implied. However, there is no comparison to alternatives or when-not-to-use guidance.
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.
v0.1.0adddividemultiplysend_to_webhooksubtractEach arithmetic tool performs a distinct operation (add, subtract, multiply, divide) and the webhook tool is clearly different. No ambiguity.
All arithmetic tools use a single verb (add, subtract, multiply, divide) and the webhook tool follows a verb_noun pattern (send_to_webhook). Consistent naming convention.
5 tools is appropriate for a focused set. The count is not excessive and the tools cover basic arithmetic plus a webhook utility. Slightly thin for an 'all-in-one' claim but still reasonable.
The arithmetic set lacks operations like modulo, exponentiation, or more complex math. The webhook tool seems disconnected. Overall, the surface is too sparse for a server named 'all-in-one'.
Unclaimed servers have limited discoverability.
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