Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/tensorfreitas/lunatask-mcp/agents-mdgit clone --depth 1 https://github.com/tensorfreitas/lunatask-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/tensorfreitas/lunatask-mcp/agents-md)<a href="https://agentmods.dev/instructions/tensorfreitas/lunatask-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/tensorfreitas/lunatask-mcp/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01640 | $0.01640 |
| Opus 5 | $0.00820 | $0.00820 |
| Sonnet 5 | $0.00328 | $0.00328 |
| Haiku 4.5 | $0.00164 | $0.00164 |
Grade A, and why
lunatask-mcp AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- lunatask-mcp CLAUDE.md — 97% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
Project Overview
LunaTask MCP is a Model Context Protocol server that provides a standardized bridge between AI models and the LunaTask API. It's designed as a lightweight, asynchronous Python application using the FastMCP framework, running as a local subprocess to enable AI tools to interact with LunaTask data.
Development Commands
Environment Setup
# Install dependencies and create virtual environment
uv sync
# Activate the virtual environment
source .venv/bin/activate
Code Quality and Testing
Always follow the guidelines in coding_guidelines
# Format and lint code
uv run ruff format
uv run ruff check --fix
# Type checking
uv run pyright
# Run all tests
uv run pytest
# Run specific test file
uv run pytest tests/test_specific_file.py
# Run tests with coverage report
uv run pytest --cov=src/lunatask_mcp --cov-report=term-missing
# Run pre-commit hooks manually
uv run pre-commit run --all-files
Running the Application
# Run the MCP server (looks for ./config.toml by default)
uv run python -m lunatask_mcp
# or using the installed script
uv run lunatask-mcp
# Run with custom config file
uv run lunatask-mcp --config-file /path/to/config.toml
# Run with debug logging
uv run lunatask-mcp --log-level DEBUG
# Get help on available options
uv run lunatask-mcp --help
Configuration
The server requires a TOML configuration file with at minimum:
lunatask_bearer_token = "your_lunatask_bearer_token_here"
See config.example.toml for a complete configuration template.
Architecture
The project follows a single, event-driven monolith architecture:
- Core Framework: FastMCP for MCP protocol handling
- Transport: stdio for client communication
- External API: HTTPS requests to LunaTask API
- Concurrency: asyncio for non-blocking I/O
- Configuration: Pydantic for settings and validation
Key architectural patterns:
- Decorator-based configuration (
@mcp.tool,@mcp.resource) - Dependency injection for configuration and HTTP clients
- Repository pattern for LunaTask API interactions
- Asynchronous I/O throughout the stack
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 175 lines · 1,640 tokens per session scan A 5577359ee36e
lunatask-mcp AGENTS.md is an instructions file published in the GitHub repository tensorfreitas/lunatask-mcp (8 stars, last pushed 5mo ago), licensed MIT. It adds 1,640 tokens to every session, about $0.0082 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
mcp AGENTS.md
AGENTS.md instructions for Teamwork/mcp, covering agents.md, this repository is public, project overview, setup commands and build and run.
raindrop-mcp AGENTS.md
Instructions for adeze/raindrop-mcp, covering raindrop mcp — codex guide, commands, architecture, non-negotiable contracts and codex workflow.
raindrop-mcp GEMINI.md
Instructions for adeze/raindrop-mcp, covering raindrop mcp server - project context, project overview, core technologies, architecture and key commands.
mcpscore AGENTS.md
AGENTS.md instructions for mcp-box/mcpscore, covering agent instructions for mcpscore and gotchas (learned the hard way).
raindrop-mcp dxt.instructions.md
Instructions for adeze/raindrop-mcp: I want to build this as an MCP Bundle, abbreviated as "MCPB". Please follow these steps.
raindrop-mcp copilot-instructions.md
Instructions for adeze/raindrop-mcp: The canonical project instructions are in AGENTS.md. Follow them for architecture, MCP safety constraints, tooling, validation, and release policy.