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Lucius MCP

Lucius MCP

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github.com/ivanostaninDeveloper Tools7Updated today

About Lucius MCP

Lucius MCP is an MCP server for AI agents and teams managing Allure TestOps test assets. Its task-specific tools cover test cases, launches, plans, defects, and metadata, with actionable hints when calls fail.

Key features

  1. 01Manage test cases and shared steps
  2. 02Create and run test plans
  3. 03Manage launches and test results
  4. 04Track defects and link them to tests
  5. 05Generate framework-specific test code
  6. 06CLI with validated actions and output formats

Use cases

  1. 01Search and update Allure TestOps test cases
  2. 02Upload results and manage launch execution
  3. 03Organize tests into suites and plans
  4. 04Track defects associated with test cases

MCP support

Lucius exposes Allure TestOps operations as focused MCP tools, including test, launch, plan, and defect management. It can be connected to clients such as Claude Desktop, Claude Code, and Codex.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
pypilucius-mcp0.16.6
ocighcr.io/ivanostanin/lucius-mcp:0.16.6
mcpblucius-mcp-0.16.6-uv.mcpb
mcpblucius-mcp-0.16.6-python.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

PyPI Version PyPI Python Version PyPI Downloads GitHub License

Allure TestOps MCP Server

Lucius is a specialized Model Context Protocol (MCP) server for Allure TestOps, built with FastMCP and Starlette.

🎯 Motivation

Allure TestOps is a powerful tool with a huge API. When you're using an AI agent to manage your tests, it can easily get lost in the details or fail because of a small technical mistake.

Lucius makes this easier by giving your AI tools that are simple to use and hard to break:

  • Clear Tools: Every tool is designed for a specific task, like "finding a test case" or "updating a launch".
  • Helpful Errors: If an AI makes a mistake, Lucius doesn't just return a code—it provides an "Agent Hint" that explains exactly what went wrong and how to fix it.
  • Solid Foundation: We follow a clean "Thin Tool" structure, meaning the logic is consistent and easy for both humans and AI to follow.

🛠️ Supported Tools

See the full reference in Tool Reference.

Tool CategoryDescriptionAll Tools
Test Case MgmtFull lifecycle for test documentation.create_test_case, update_test_case, delete_test_case, delete_archived_test_cases, get_test_case_details, get_test_case_custom_fields
Automation GenerationGenerate framework-specific code from existing test cases.generate_test_code
Search & DiscoveryAdvanced search and project metadata discovery.list_test_cases, search_test_cases, get_custom_fields, list_integrations, get_project
Shared StepsCreate and manage reusable step sequences.create_shared_step, list_shared_steps, update_shared_step, delete_shared_step, delete_archived_shared_steps, link_shared_step, unlink_shared_step
Test LayersManage test taxonomy and auto-mapping schemas.list_test_layers, create_test_layer, update_test_layer, delete_test_layer, list_test_layer_schemas, create_test_layer_schema, update_test_layer_schema, delete_test_layer_schema
Test HierarchyOrganize suites and assign tests in tree paths.create_test_suite, list_test_suites, assign_test_cases_to_suite, delete_test_suite
Custom FieldsProject-level management of custom field values.list_custom_field_values, create_custom_field_value, update_custom_field_value, delete_custom_field_value, delete_unused_custom_fields
Launch ManagementManage launches, result uploads, manual execution, reruns, and attachments.create_launch, list_launches, get_launch, list_launch_test_results, upload_test_results, attach_file_to_launch, rerun_test_results_manually, start_manual_test_session, submit_manual_test_results, add_test_result_attachment
Test Result ManagementInspect one exact TestOps result and prepare verified evidence downloads.get_test_result, prepare_attachment_download
Test PlansManage test plans and their content.create_test_plan, update_test_plan, delete_test_plan, list_test_plans, manage_test_plan_content, run_test_plan
Defect MgmtTrack defects, linkage, and automation rules.create_defect, get_defect, update_defect, delete_defect, list_defects, link_defect_to_test_case, unlink_issue_from_test_case, list_defect_test_cases, create_defect_matcher, list_defect_matchers, update_defect_matcher, delete_defect_matcher

🚀 Quick Start

  1. Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh
  2. Setup Credentials: Create a .env file with the variables below, or save CLI auth with lucius auth.
  3. Run Server: uv run start
Basic .env for Quick Start
VariableDescriptionExample
ALLURE_ENDPOINTAllure TestOps base URLhttps://example.testops.cloud
ALLURE_PROJECT_IDDefault Allure project ID (optional for get_project; required by project-scoped tools)123
ALLURE_API_TOKENAllure API token<your_api_token>
MCP_MODEMCP transport mode for Lucius runtimestdio
🔌 Claude Desktop Integration

The easiest way to use Lucius in Claude Desktop is via the .mcpb bundle:

  1. Download the latest lucius-mcp.mcpb from Releases.
  2. Open with Claude Desktop.
  3. Configure your Allure credentials in the UI.
💻 Claude Code Integration

To add Lucius to Claude Code, use the following command from within your project directory:

claude mcp add --transport stdio --scope project \
  --env ALLURE_ENDPOINT=https://example.testops.cloud \
  --env ALLURE_PROJECT_ID=123 \
  --env ALLURE_API_TOKEN=<your_api_token> \
  --env MCP_MODE=stdio \
  testops-mcp -- uvx --from lucius-mcp --refresh start

Project-scoped text config example (.mcp.json):

{
  "mcpServers": {
    "testops-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "lucius-mcp",
        "--refresh",
        "start"
      ],
      "env": {
        "ALLURE_ENDPOINT": "https://example.testops.cloud",
        "ALLURE_PROJECT_ID": "123",
        "ALLURE_API_TOKEN": "<your_api_token>",
        "MCP_MODE": "stdio"
      }
    }
  }
}
🧠 Codex Integration

To add Lucius to Codex (CLI or IDE extension), use:

codex mcp add testops-mcp \
  --env ALLURE_ENDPOINT=https://example.testops.cloud \
  --env ALLURE_PROJECT_ID=123 \
  --env ALLURE_API_TOKEN=<your_api_token> \
  --env MCP_MODE=stdio \
  -- uvx --from lucius-mcp --refresh start

Text config example (~/.codex/config.toml or project .codex/config.toml):

[mcp_servers.testops-mcp]
command = "uvx"
args = ["--from", "lucius-mcp", "--refresh", "start"]

[mcp_servers.testops-mcp.env]
ALLURE_ENDPOINT = "https://example.testops.cloud"
ALLURE_PROJECT_ID = "123"
ALLURE_API_TOKEN = "<your_api_token>"
MCP_MODE = "stdio"

For detailed setup, including Claude Desktop (MCPB) integration, see Setup Guide.

🐍 Supported Python versions

Lucius supports Python 3.10 through 3.14 for runtime use. The generated MCPB manifests and the representative Nuitka CLI compiler matrix validate the same range. Python 3.15 support is experimental: installation may require source builds, and platform compatibility is still being verified. See the development guide for current limitations. Python 3.9 remains unsupported because the pinned Starlette requirement needs Python 3.10 or newer.

💻 Command-Line Interface (CLI)

Lucius also provides a universal CLI entry point for direct tool execution from the command line:

# List available actions for an entity
uv run lucius test_case

# Execute an action
uv run lucius test_case get --args '{"test_case_id": 1234}'

# Show help for a specific entity/action
uv run lucius test_case get --help

# Save reusable CLI auth
uv run lucius auth --url https://example.testops.cloud --token <your_api_token> --project 123
uv run lucius auth status
uv run lucius auth clear

CLI Features:

  • 🎯 Type-safe entity/action invocation with validation
  • 🔐 Optional persistent CLI auth with native per-user config storage
  • 📊 Multiple output formats (JSON, table, csv, plain)
  • 🔍 Per-action help with parameters and examples
  • 🛡️ Clean error messages with guidance
  • 📦 Standalone binaries for Linux, macOS, and Windows

CLI auth precedence is:

  1. Explicit tool args such as api_token or project_id
  2. Environment variables
  3. Saved CLI auth config from uv run lucius auth
  4. Defaults

Saved CLI auth uses native config locations:

  • Linux/Unix: $XDG_CONFIG_HOME/lucius/auth.json or ~/.config/lucius/auth.json
  • macOS: ~/Library/Application Support/lucius/auth.json unless XDG overrides are explicitly set
  • Windows: %LOCALAPPDATA%\lucius\auth.json

For full CLI documentation and installation instructions, see CLI Guide.

📡 Telemetry

Lucius collects privacy-preserving usage telemetry to improve tool quality. Telemetry is enabled by default and sends metadata to https://stats.ostanin.me, an endpoint operated by the project owner (no third party has access to this endpoint).

If this is acceptable in your environment, staying opted in helps improve Lucius over time. If you want to opt out, set TELEMETRY_ENABLED=false in your environment.

No API tokens, test content, or tool arguments are sent.

See Telemetry & Privacy for the full data dictionary and telemetry behavior details.

📂 Documentation

Full documentation is available in the docs/ folder:

🤝 Contributing

Contributions are welcome! Please see the Contribution Guidelines and the Development Guide for more details.

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New to MCP?

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.