MCP SERVER LIMS

@sheffler

Basic information

About MCP SERVER LIMS

MCP Tools use for a Laboratory Information Management System

MCP SERVER LIMS config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-lims-server": {
      "command": "/Users/sheffler/git/mcp-server-lims/.venv/bin/python",
      "args": [
        "/Users/sheffler/git/mcp-server-lims/lims_server.py"
      ]
    },
    "sqlite": {
      "command": "/Users/sheffler/.local/bin/uvx",
      "args": [
        "mcp-server-sqlite",
        "--db-path",
        "/Users/sheffler/test.db"
      ]
    }
  }
}

MCP SERVER LIMS tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP SERVER LIMS?

MCP SERVER LIMS implements a simulated laboratory workflow for an AI Agent to manage sample data across accessioning, preparation, analysis, and report generation steps. It provides MCP tools that mimic instrument interfaces with rich JSON Schema types, intended for use with Anthropic’s Claude or other MCP clients. The server is accompanied by a companion SQLite database server.

How to use MCP SERVER LIMS?

Clone the repository, set up a Python virtual environment with uv, and install dependencies. Configure the server in Claude Desktop or Oterm by editing the respective JSON config file to point to lims_server.py. The server must be run alongside mcp-server-sqlite.

Key features of MCP SERVER LIMS

Simulates two laboratory instruments (preparation and analyzer).
Tools accept and return arrays of structured sample data.
Random generation of tags, pass/fail status, and metrics.
Integrates with MCP clients via standard tool-calling protocol.
Includes sample prompts for full workflow or smaller tool tests.

Use cases of MCP SERVER LIMS

Demonstrating an AI agent orchestrating multi-step laboratory data workflows.
Testing LLM ability to manage richly structured data in tool calls.
Prototyping integration of real lab instruments with MCP.

Frequently asked questions

What runtime dependencies does MCP SERVER LIMS require?

The server requires `uv` for setup and the MCP Python SDK. It also needs `mcp-server-sqlite` running for database access.

How are instrument results generated?

The preparation tool randomly assigns a genetic tag and a “passed” or “failed” status per sample. The analyzer tool randomly produces a metric value.

Can I use MCP SERVER LIMS without a database?

The example workflow assumes a database is available for storing intermediate results; the AI agent may use the SQLite server to save data.

What transports or authentication does the server use?

The README does not specify any transport or authentication; it suggests direct local configuration with Claude Desktop or Oterm via stdio.

Are there limits on the number of samples?

The README does not state an explicit limit; the tools accept an array of samples.

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