activitywatch-mcp-server-py: Instructions file for Codex

AGENTS.md

activitywatch-mcp-server-py AGENTS.md is an instructions file for Codex, OpenCode from Jelloeater/activitywatch-mcp-server-py. It costs 3,362 tokens per session, scanned A, original, MIT.

Repository instructions for a Python MCP server that connects AI coding assistants to ActivityWatch, an application that records computer activity and time use. It describes the server's architecture and command-line entry point.

In plain words
What is it for?
Use it when developing, testing, packaging, or extending the ActivityWatch MCP server and its time-tracking tools.
Why use it?
It gives agents the project structure and connection details needed to change the server without breaking communication with ActivityWatch or MCP clients.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md; mentions OpenCode.

This is Jelloeater/activitywatch-mcp-server-py's own configuration. It tells Codex and OpenCode how to work on activitywatch-mcp-server-py itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything activitywatch-mcp-server-py configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Jelloeater/activitywatch-mcp-server-py. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Jelloeater/activitywatch-mcp-server-py/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/Jelloeater/activitywatch-mcp-server-py

Made for: Codex, OpenCode.

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README.md
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Per session 3,362 This file is loaded in full into every session.
When invoked 3,362 The same file — it is already loaded in full.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.03362 $0.03362
Opus 5 $0.01681 $0.01681
Sonnet 5 $0.00672 $0.00672
Haiku 4.5 $0.00336 $0.00336

Measured 8d ago against content hash 11e4d6f9a841, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

activitywatch-mcp-server-py AGENTS.md scanned grade A with 1 finding 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 8d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- Always use `urllib.parse.quote(value, safe="")` for path parameters
AGENTS.md · 409 lines

How it starts

The opening of the file, as written. The whole thing — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md - AI Agent Context for ActivityWatch MCP Server

This document provides context for AI agents (LLMs, coding assistants) working on this project. It contains architecture decisions, conventions, and guidance for future improvements.

Project Overview

Repository: https://github.com/8bitgentleman/activitywatch-mcp-server
Purpose: Model Context Protocol (MCP) server that connects LLMs to ActivityWatch time tracking API
Language: Python 3.10+
Version: 2.0.0 (migrated from TypeScript)
Package Name: mcp-server-activitywatch
Entry Point: mcp-server-activitywatch command

Architecture

High-Level Structure

┌─────────────────┐
│  MCP Client     │ (Claude Desktop, OpenCode, Crush)
│  (LLM)          │
└────────┬────────┘
         │ MCP Protocol (stdio)
         │
┌────────▼────────────────────────────────────────────┐
│  MCP Server (Python)                                │
│  ┌──────────────────────────────────────────────┐  │
│  │  server.py                                   │  │
│  │  - Stdio transport                           │  │
│  │  - Tool registration                         │  │
│  │  - Request routing                           │  │
│  └──────────────────┬───────────────────────────┘  │
│                     │                               │
│  ┌──────────────────▼───────────────────────────┐  │
│  │  tools/                                      │  │
│  │  - list_buckets.py                           │  │
│  │  - run_query.py                              │  │
│  │  - get_events.py                             │  │
│  │  - get_settings.py                           │  │
│  │  - query_examples.py                         │  │
│  └──────────────────┬───────────────────────────┘  │
└───────────────────┬─┴────────────────────────────────┘
                    │ HTTP (httpx)
         ┌──────────▼──────────┐
         │  ActivityWatch API  │
         │  localhost:5600     │
         └─────────────────────┘

Key Components

Read the full file on GitHub · 409 lines

Changes

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.

  1. 8d ago First seen · 409 lines · 3,362 tokens per session scan A 11e4d6f9a841

Subscribe to this mod's changes

activitywatch-mcp-server-py AGENTS.md is an instructions file published in the GitHub repository Jelloeater/activitywatch-mcp-server-py (4 stars, last pushed 2mo ago), licensed MIT. It adds 3,362 tokens to every session, about $0.0168 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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