MCP_Based_AI_Assistant
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_Based_AI_Assistantsearch our documents for the remote work policy"
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_Based_AI_Assistant
An AI assistant built using Model Context Protocol (MCP) and LangGraph. The assistant can understand user queries, determine whether a tool is required, and invoke the appropriate MCP tool through an MCP client.
MCP server with multiple tools
MCP client for connecting to the MCP server
MCP tool discovery
LangGraph-based stateful agent workflow
LLM-based tool selection
Conditional tool execution
Human-in-the-Loop approval for sensitive tools
Document search
Employee information lookup
Mathematical expression calculation
LLM-based final response generation
Test cases for validating different queries and tool workflows
Architecture

Related MCP server: Enterprise Architecture MCP Server
Installation
1. Clone the repository
git clone <your-repository-url>
cd MCP_Based_AI_Assistant
2. Create a virtual environment
uv venv3. Activate the virtual environment
Windows PowerShell
.venv\Scripts\Activate.ps14. Install dependencies
uv sync5. Configure environment variables
Create a .env file in the project root directory:
GEMINI_API_KEY=your_gemini_api_key6. Run the test cases
uv run python -m tests.testTool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/Archana-M-11/Mcp_Based_AI_Assistant'
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