Mcp Server Operator

B+ 88 completed
Other
monorepo / python · tiny
43
Files
3,382
LOC
1
Frameworks
5
Languages

Pipeline State

completed
Run ID
#388077
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
35.59
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #48767
Member of a group with 1 similar repo(s) — canonical #56392 view group →
Top concepts (2)
Project DescriptionTesting
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AI Prompt

Create a Python-based subordinate machine charm for Juju that deploys a Model Context Protocol (MCP) server. This server should allow principal charms to declaratively expose tools, prompts, and resources to MCP clients, like LLM agents. The charm needs to read tool definitions, which are provided as JSON in the `mcp` relation's app data bag. Implement support for two handler types: `exec` (running shell commands) and `http` (calling local HTTP endpoints). The system should integrate with Traefik for ingress and use the MCP Python SDK to run the server as a systemd service.
python juju charm mcp llm agent systemd tooling declarative web-service
Generated by gemma4:latest

Catalog Information

A subordinate machine charm that deploys a Model Context Protocol (MCP) server, allowing principal charms to declaratively expose tools, prompts, and resources to MCP clients (LLM agents, Claude Code, etc.) — without implementing MCP themselves.

Description

A subordinate machine charm that deploys a Model Context Protocol (MCP) server, allowing principal charms to declaratively expose tools, prompts, and resources to MCP clients (LLM agents, Claude Code, etc.) — without implementing MCP themselves.

Novelty

3/10

Tags

python juju charm mcp llm agent systemd tooling declarative web-service

Claude Models

claude-opus-4-6

Quality Score

B+
88.5/100
Structure
93
Code Quality
85
Documentation
90
Testing
85
Practices
83
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (70% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Security & Health

4.1h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (3)
Repobility · severity-and-effort ranking · https://repobility.com
Apache-2.0
License
39.1%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
53.3%
markdown
24.4%
yaml
11.4%
toml
7.0%
ini
3.9%

Frameworks

pytest

Concepts (2)

Repobility · code-quality scanner for AI-generated software · https://repobility.com
CategoryNameDescriptionConfidence
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
auto_descriptionProject DescriptionA subordinate machine charm that deploys a Model Context Protocol (MCP) server, allowing principal charms to declaratively expose tools, prompts, and resources to MCP clients (LLM agents, Claude Code, etc.) — without implementing MCP themselves.80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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