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AIE8-MCP Session Server

by kchia
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Python
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AI Makerspace: MCP Session Repo for Session 13

This project demonstrates an MCP (Model Context Protocol) server with LangGraph integration, utilizing the Tavily API for web search capabilities and other useful tools.

Related MCP server: MCP MyZ Search

Project Overview

The MCP server provides multiple tools and is integrated with a LangGraph application for intelligent workflow orchestration.

Prerequisites

  • Python 3.13 or higher

  • A valid Tavily API key

  • A valid OpenAI API key

⚠️NOTE FOR WINDOWS:⚠️

You'll need to install this on the Windows side of your OS.

This will require getting two CLI tool for Powershell, which you can do as follows:

  • winget install astral-sh.uv

  • winget install --id Git.Git -e --source winget

After you have those CLI tools, please open Cursor into Windows.

Then, you can clone the repository using the following command in your Cursor terminal:

git clone https://AI-Maker-Space/AIE8-MCP-Session.git

After that, you can follow from Step 2. below!

Installation

  1. Clone the repository:

    git clone <repository-url>
cd <repository-directory>

  2. Configure environment variables: Copy the .env.sample to .env and add your API keys:

    TAVILY_API_KEY=your_tavily_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

  3. Set up the environment:

    uv run python setup_env.py

Running the MCP Server

To start the MCP server, you will need to add the following to your MCP Profile in Cursor:

NOTE: To get to your MCP config. you can use the Command Pallete (CMD/CTRL+SHIFT+P) and select "View: Open MCP Settings" and replace the contents with the JSON blob below.

{
    "mcpServers":  {
        "mcp-server": {
            "command" : "uv",
            "args" : ["--directory", "/PATH/TO/REPOSITORY", "run", "server.py"]
        }
    }
}

The server will start and listen for commands via standard input/output.

Activities:

🏗️ Activity #1: ✅ COMPLETED

Choose an API that you enjoy using - and build an MCP server for it!

MCP Server Features:

  • Web search using Tavily API

  • Dice rolling with custom notation

  • Text processing utilities

  • Random dad jokes

🏗️ Activity #2: ✅ COMPLETED

Build a simple LangGraph application that interacts with your MCP Server.

Simple Solution:

  • langgraph_simple_final.py - The complete LangGraph application

  • setup_env.py - Helper script for environment setup

  • SIMPLE_SOLUTION.md - Complete documentation

Quick Start:

# 1. Set up environment
uv run python setup_env.py

# 2. Add your OpenAI API key to .env file
# 3. Run the application
uv run python langgraph_simple_final.py

# Or run interactive mode
uv run python langgraph_simple_final.py --interactive

Features:

  • ✅ Single file solution - Everything in one Python file

  • ✅ LLM-powered - Uses OpenAI GPT-4o-mini with API key

  • ✅ All MCP tools integrated - Web search, dice rolling, text processing, jokes

  • ✅ Automatic tool selection - LLM chooses appropriate tools based on input

  • ✅ Interactive mode - Real-time conversation with the system

  • ✅ Type-safe state management - Uses TypedDict for reliability

Architecture:

  • Single LangGraph node that processes user input

  • Direct MCP function imports (no subprocess complexity)

  • LangChain tool binding for seamless integration

  • OpenAI API integration for intelligent responses

Usage

The LangGraph application provides an intelligent interface to all MCP server tools. Simply run the application and ask it to:

  • Search the web for information

  • Roll dice with custom notation

  • Process text (uppercase, lowercase, reverse, etc.)

  • Tell you a joke

The LLM will automatically choose the appropriate tools and provide intelligent responses based on your requests.

Additional Files

Testing

  • test_server.py - Comprehensive test script for the MCP server

    • Tests all MCP functions directly

    • Tests MCP server via subprocess communication

    • Run with: uv run python test_server.py

Advanced Features

  • dice_roller_numpy.py - Advanced dice rolling utility using NumPy

    • Better performance for large numbers of dice

    • Statistical analysis of rolls

    • Supports complex notation (e.g., "3d8+2", "1d20-1")

    • Run with: uv run python dice_roller_numpy.py

Project Structure

AIE8-MCP-Session/
├── langgraph_simple_final.py    # Main LangGraph application
├── setup_env.py                 # Environment setup helper
├── server.py                    # MCP server (Activity #1)
├── dice_roller.py              # Basic dice rolling utility
├── dice_roller_numpy.py        # Advanced NumPy dice roller
├── test_server.py              # Test script for MCP server
├── pyproject.toml              # Dependencies
├── README.md                   # Main documentation
├── SIMPLE_SOLUTION.md          # Simple solution guide
└── uv.lock                     # Lock file

Available Tools

4 tools
get_quoteB

Get a random dad joke from the icanhazdadjoke API.

Returns: A random dad joke

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions the external API but does not state that the operation is read-only, that it requires network access, or what happens on failure (e.g., empty result or error). This is minimal disclosure beyond the core action.

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

Conciseness3/5

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

The description is short but contains redundancy: the first sentence already states it gets a random dad joke, and the second sentence repeats the same information with a 'Returns:' label. Since an output schema exists, this second sentence adds no value and could be removed.

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?

For a simple parameterless tool with an output schema, the essential behavior is covered. However, the description omits potential operational context such as network dependency, error handling, or rate limits, and the tool name 'get_quote' may mislead users expecting quotes instead of dad jokes. This leaves room for improvement.

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 tool has zero parameters, and the input schema is an empty object with 100% coverage. Per the rubric, a baseline of 4 applies to parameterless tools, and the description correctly does not need to elaborate on parameters.

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

Purpose5/5

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

The description clearly states the tool fetches a random dad joke from a specific API (icanhazdadjoke), using the verb 'Get' with a concrete resource. This is distinct from sibling tools such as web_search and roll_dice, leaving no ambiguity about its function.

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?

There is no information about when to use this tool compared to alternatives. It does not mention exclusions, prerequisites, or contexts where it would be preferred over roll_dice or web_search, offering no usage guidance.

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

roll_diceC

Roll the dice with the given notation

ParametersJSON Schema
NameRequiredDescriptionDefault
notationYes
num_rollsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full responsibility for disclosing behavior. It only states that dice are rolled, without mentioning randomness, error handling for invalid notation, return format, or how num_rolls affects output. 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.

Conciseness4/5

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

The description consists of one short, direct sentence with no redundant wording, achieving excellent conciseness. However, the brevity sacrifices essential details, making it slightly less 'appropriately sized' for the tool's two-parameter surface.

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

Completeness2/5

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

Given two parameters, no annotations, and an output schema, the description should clarify the expected notation syntax and what the tool returns (e.g., individual rolls, aggregate totals). It does neither, leaving basic usage ambiguous for an agent that needs to invoke this tool correctly.

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

Parameters2/5

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

The description refers only to 'given notation' and does not explain its expected format (e.g., dice notation) or the role of num_rolls. Schema description coverage is 0%, so the parameter meanings must be guessed solely from their names, which is insufficient.

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 identifies the action (roll dice) and the key input (notation), making it distinct from unrelated sibling tools like web_search or get_quote. However, it does not explicitly clarify that 'notation' follows dice notation (e.g., '2d6'), leaving some ambiguity for an agent unfamiliar with RPG conventions.

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?

No guidance is given about when to use this tool or when to prefer alternatives. The sibling tools are unrelated, so there is no explicit contrast or scenario described, leaving the agent to infer the appropriate context solely from the name.

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

text_utilsA

Perform various text operations on the input text.

Available operations:

  • 'uppercase': Convert text to uppercase

  • 'lowercase': Convert text to lowercase

  • 'title': Convert text to title case

  • 'reverse': Reverse the text

  • 'word_count': Count number of words

  • 'char_count': Count number of characters

  • 'remove_spaces': Remove all spaces

  • 'add_spaces': Add spaces between each character

  • 'random_case': Randomize the case of each character

ParametersJSON Schema
NameRequiredDescriptionDefault
textYes
operationYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description takes on the burden of disclosing behavior. It thoroughly describes each operation's effect (e.g., 'uppercase: Convert text to uppercase') and notes the random_case behavior. However, it does not mention edge cases such as empty input or invalid operation handling, nor does it describe the return structure, though an output schema exists. This is adequate for a pure function.

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 efficiently structured with a one-line intro followed by a bulleted list of operations. Every line conveys necessary information, and the format is front-loaded with the primary purpose. No wasteful words.

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

Completeness5/5

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

Given that the tool has only two simple parameters and an output schema, the description is complete. It covers all operation options and their semantics, making it sufficient for an agent to select and invoke the tool. The lack of detailed error handling or whitespace notes is a minor omission for such a deterministic utility.

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

Parameters5/5

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

The input schema provides only parameter names and types with zero descriptions. The description compensates fully by explaining the 'operation' parameter enumerations and their meanings, and by defining 'text' as the 'input text.' This gives the agent everything needed to construct valid arguments.

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

Purpose5/5

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

The description opens with 'Perform various text operations on the input text,' a specific verb+resource statement that immediately defines the tool's scope. It enumerates nine distinct operations, distinguishing it from sibling tools like web_search and roll_dice, which are unrelated. The tool name 'text_utils' is vague, but the description fully disambiguates it.

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

Usage Guidelines4/5

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

The description clearly frames the tool's use case as text transformation and lists all supported operations, giving an agent precise criteria for selection. It does not explicitly state when NOT to use this tool, but the sibling tools are so different that no exclusions are necessary. The context is unambiguous.

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. 4 tool updatesv0.1.0
    • First observedget_quote
    • First observedroll_dice
    • First observedtext_utils
    • First observedweb_search
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TDQS

B3.3/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: web search, dice rolling, text manipulation, and getting a joke. There is no overlap or ambiguity between them.

Naming Consistency4/5

Three tools follow a verb_noun pattern (web_search, roll_dice, get_quote), but text_utils is a noun_noun exception. The inconsistency is minor and the names remain intuitive.

Tool Count4/5

Four tools is a reasonable number, but the tools cover unrelated domains, making the set feel scattered rather than cohesively scoped. Still, each tool earns its place.

Completeness2/5

There is no coherent domain to assess completeness against. For a 'session server', the tools are arbitrary and many common utilities (e.g., math, formatting) are absent, so obvious gaps exist.

Maintenance

ActivityInactive
ResponsivenessNo issues

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