MCP Server Deepdive
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 Deepdiveshow me how to install and run this server"
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.
MCP Server Deepdive Deployment
Installation Steps
1. Clone the Repository
git clone https://github.com/KrishnaPapana/mcpserverexample.git
cd mcpserverexample2. Set Up Python Environment
Ensure you have Python 3.12 or higher installed.
(Recommended) Create and activate a virtual environment:
python -m venv .venv
.venv\Scripts\activate3. Install Dependencies
pip install -U pip
pip install .4. Run the MCP Server
mcp-serverThis will start the MCP server using the entry point defined in pyproject.toml.
For more details, see the project documentation or source code.
Related MCP server: MCP Builder
MCP Client Configuration
You can add the following to your MCP client config file to automatically install and run the server:
{
"mcpserver":{
"server":{
"command": "uv",
"args":[
"--from",
"git+https://github.com/KrishnaPapana/mcpserverexample.git",
"mcp-server"
]
}
}
}Available Tools
1 tooladd_two_numbersC
Adds two numbers together.
Args:
a -- the first number
b -- the second number
Returns:
The sum of a and b
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | ||
| b | Yes |
TDQS
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 mentions the return value ('The sum of a and b'), which adds some context, but fails to address important aspects like error handling, input validation, or performance characteristics. For a tool with zero annotation coverage, this is insufficient.
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 appropriately sized and well-structured with clear sections for purpose, arguments, and returns. It uses minimal sentences that each serve a distinct purpose, though the formatting could be slightly more polished.
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 (simple arithmetic) and lack of annotations or output schema, the description is moderately complete. It covers the basic purpose and parameters but misses details like error cases or input constraints, which would be helpful for an AI agent to use it correctly.
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 explicitly defines parameters 'a' and 'b' as 'the first number' and 'the second number', adding meaning beyond the input schema (which has 0% description coverage and only specifies types as strings). However, it does not clarify that the parameters are strings in the schema or explain how numeric addition works with string inputs, leaving some ambiguity.
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 ('Adds') and resource ('two numbers together'), making it immediately understandable. However, since there are no sibling tools, 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.
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 or in what context it should be applied. It simply states what the tool does without any usage instructions, prerequisites, or exclusions.
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 tool update
- First observed
add_two_numbers
TDQS
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool 'add_two_numbers' has a single, clear purpose that cannot be confused with any other functionality.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'add_two_numbers' follows a clear verb_noun pattern, which would be consistent if more tools existed.
A single tool is generally too few for most server purposes, as it limits functionality and suggests an incomplete or trivial implementation. For a server named 'Deepdive', which implies comprehensive exploration, one tool is a significant mismatch in scope.
The server is severely incomplete, as it only provides a basic arithmetic operation with no coverage of any broader domain. There is no indication of what 'Deepdive' entails, but a single addition tool fails to support any meaningful workflows or use cases.
Resources
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