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MCP Server Example

by alejandro-ao
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Python
Hybrid

MCP Server Example

This repository contains an implementation of a Model Context Protocol (MCP) server for educational purposes. This code demonstrates how to build a functional MCP server that can integrate with various LLM clients.

To follow the complete tutorial, please refer to the YouTube video tutorial.

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools.

MCP Diagram

Key Benefits

  • A growing list of pre-built integrations that your LLM can directly plug into

  • Flexibility to switch between LLM providers and vendors

  • Best practices for securing your data within your infrastructure

Related MCP server: Optimized Memory MCP Server V2

Architecture Overview

MCP follows a client-server architecture where a host application can connect to multiple servers:

  • MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP

  • MCP Clients: Protocol clients that maintain 1:1 connections with servers

  • MCP Servers: Lightweight programs that expose specific capabilities through the standardized Model Context Protocol

  • Data Sources: Both local (files, databases) and remote services (APIs) that MCP servers can access

Core MCP Concepts

MCP servers can provide three main types of capabilities:

  • Resources: File-like data that can be read by clients (like API responses or file contents)

  • Tools: Functions that can be called by the LLM (with user approval)

  • Prompts: Pre-written templates that help users accomplish specific tasks

System Requirements

  • Python 3.10 or higher

  • MCP SDK 1.2.0 or higher

  • uv package manager

Getting Started

Installing uv Package Manager

On MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.

Project Setup

  1. Create and initialize the project:

# Create a new directory for our project
uv init mcp-server
cd mcp-server

# Create virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install dependencies
uv add "mcp[cli]" httpx

  1. Create the server implementation file:

touch main.py

Running the Server

  1. Start the MCP server:

uv run main.py

  1. The server will start and be ready to accept connections

Connecting to Claude Desktop

  1. Install Claude Desktop from the official website

  2. Configure Claude Desktop to use your MCP server:

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
    "mcpServers": {
        "mcp-server": {
            "command": "uv",  # It's better to use the absolute path to the uv command
            "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/YOUR/mcp-server",
                "run",
                "main.py"
            ]
        }
    }
}

  1. Restart Claude Desktop

Troubleshooting

If your server isn't being picked up by Claude Desktop:

  1. Check the configuration file path and permissions

  2. Verify the absolute path in the configuration is correct

  3. Ensure uv is properly installed and accessible

  4. Check Claude Desktop logs for any error messages

License

This project is licensed under the MIT License. See the LICENSE file for details.

Available Tools

1 tool
get_docsB

Search the latest docs for a given query and library. Supports langchain, openai, and llama-index.

Args: query: The query to search for (e.g. "Chroma DB") library: The library to search in (e.g. "langchain")

Returns: Text from the docs

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
libraryYes

TDQS

B3.3/5.0
Behavior2/5

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. It mentions 'latest docs' and 'search,' but fails to describe critical behaviors such as authentication needs, rate limits, pagination, error handling, or what 'latest' means (e.g., versioning or update frequency). This leaves significant gaps for a search tool.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first, followed by supported libraries and parameter details in a structured 'Args' and 'Returns' format. It avoids redundancy, but the 'Returns' section is vague ('Text from the docs'), slightly reducing efficiency.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose, parameters, and return type broadly, but lacks details on behavioral aspects like search scope, result format, or error conditions, leaving room for improvement in guiding an AI agent.

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

Parameters4/5

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

The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'query' is for searching (e.g., 'Chroma DB') and 'library' specifies the target (e.g., 'langchain'), including examples and listing supported libraries. This compensates well for the schema's lack of descriptions, though it could detail format constraints (e.g., case sensitivity).

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: 'Search the latest docs for a given query and library.' It specifies the verb ('search'), resource ('docs'), and scope ('latest docs for a given query and library'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage context by listing supported libraries ('langchain, openai, and llama-index'), which suggests when to use this tool. However, it lacks explicit guidance on when not to use it or alternatives, and does not mention prerequisites or constraints beyond the library options.

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 observedget_docs
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TDQS

B3.4/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose of searching documentation for specific libraries.

Naming Consistency5/5

The single tool name 'get_docs' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations to evaluate.

Tool Count2/5

A single tool is too few for most server purposes, as it severely limits functionality and scope. This feels thin and incomplete for a documentation search server, which might benefit from additional tools like browsing documentation structure or getting library lists.

Completeness2/5

The tool surface is severely incomplete for a documentation search domain. It only supports searching text, with no tools for browsing, listing available libraries, or accessing documentation metadata, creating significant gaps that will hinder agent workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

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