Panteon Timeline MCP Server 🚀

An MCP (Model Context Protocol) server built with FastMCP that enables AI assistants (Antigravity, Claude Desktop, Cursor, etc.) to fetch real-time task data and conversation history directly from Panteon Timeline (https://timeline.panteon.no).

✨ Features

• Extract Issue ID: Automatically parses URL formats like https://timeline.panteon.no/tasks#/list/15033/edit/5461 or accepts direct issue IDs (5461).

• Two-Step API Resolution: Mirrors the browser extension logic by resolving the external issue ID (5461) to Panteon's internal task_id via API 1, and then fetching the complete conversation timeline via API 2.

• AI-Optimized Formatting: Provides structured XML/Markdown context (<task_title>, <task_description>, <conversation_history>) ready for AI analysis, summarization, and reply drafting.

• Flexible Authentication: Supports JWT Bearer tokens or Cookie headers via .env file configuration or per-tool-call parameters.

Related MCP server: ClickUp MCP

🛠️ Installation & Setup

1. Install Dependencies

Ensure you have Python 3.10+ installed, then create or reuse the project virtualenv and install dependencies into it:

cd timeline-mcp
python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt

2. Configure Authentication

To fetch protected data from timeline.panteon.no, the MCP server needs an authentication token from your logged-in browser session.

1. Copy .env.example to .env:

   cp .env.example .env

2. Open Google Chrome and log in to https://timeline.panteon.no.

3. Open Chrome DevTools (F12 or Ctrl+Shift+I) -> navigate to the Network tab.

4. Refresh the timeline task page or perform any action.

5. Click on any request starting with api/ (e.g., taskbyissue/... or timeline).

6. Under Request Headers, copy the value of Authorization (without the Bearer prefix) or the raw Cookie header string.

7. Paste it into your .env file:

   PANTEON_BEARER_TOKEN=eyJ...
   # OR
   PANTEON_COOKIE=...

🤖 Configuring MCP Clients

For Claude Desktop / Antigravity / Cursor

Add the following configuration to your MCP server configuration file (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "timeline-mcp": {
      "command": "/home/harpalsinh.solanki@hs.local/projects/panteon/timeline-mcp/.venv/bin/python",
      "args": [
        "/home/harpalsinh.solanki@hs.local/projects/panteon/timeline-mcp/server.py"
      ],
      "env": {
        "PANTEON_BEARER_TOKEN": "your_jwt_token_here"
      }
    }
  }
}

For Codex

Codex reads MCP servers from config.toml. Add this to ~/.codex/config.toml for a global setup, or to .codex/config.toml inside this repository for a project-scoped setup:

[mcp_servers.timeline_mcp]
command = "/home/harpalsinh.solanki@hs.local/projects/panteon/timeline-mcp/.venv/bin/python"
args = ["/home/harpalsinh.solanki@hs.local/projects/panteon/timeline-mcp/server.py"]
cwd = "/home/harpalsinh.solanki@hs.local/projects/panteon/timeline-mcp"
startup_timeout_sec = 20
tool_timeout_sec = 120

With cwd set to this repository, python-dotenv loads .env automatically. If you prefer to pass credentials from the shell instead, add:

env_vars = ["PANTEON_BEARER_TOKEN", "PANTEON_COOKIE", "PANTEON_TIMELINE_DIR"]

After saving the config, restart Codex and run /mcp in the Codex TUI to confirm timeline_mcp is active.

🧰 Available Tools

get_task_conversation

Fetches a task in full — its description body and the entire comment conversation — including every image embedded anywhere, and writes it to a markdown file.

• Arguments:

  • issue_identifier (str) — e.g. "https://timeline.panteon.no/tasks#/list/15033/edit/5461" or "5461". Must be a bare numeric ID or a URL containing /edit/<id>.

  • bearer_token / cookie (str, optional) — override the .env credentials per call.

  • inline (bool, default false) — when true, the full task markdown is appended to the return value in addition to being saved. Use this for clients that can't read the local filesystem path.

• Side effect: writes timeline/<issue_id>/task.md (title, metadata, description, comments). The saved file is also exposed as an MCP resource at timeline://<issue_id>/task.md, so clients can read the full content through MCP after a fetch.

• Empty tasks: when both description and comments are empty, the tool asks for optional user-provided context if the MCP client supports elicitation. Accepted input is saved under ## User-Provided Context; unsupported clients keep the normal placeholder output.

• Returns a short human-readable summary string with the issue id, title, status, and saved file path — for example:

  Issue #4117 — Label print
  Status:     open
  Saved to:   /abs/path/timeline/4117/task.md

  The full task body, the entire comment conversation, and every image URL are written to the saved file. Read that file to access the full content.

Image URLs embedded in comments (markdown ![](…) or HTML <img>) are resolved to absolute https://timeline.panteon.no/... URLs. Non-image attachment links (e.g. .csv) are excluded.

Output location

The file is written to <base>/timeline/<issue_id>/task.md, where <base> is:

1. PANTEON_TIMELINE_DIR env var, if set (this MCP is configured with it pointing at the duell-admin project root); otherwise

2. the current working directory.

💻 Command-Line Usage

The same fetch logic is available from the shell via fetch_task.py (credentials come from .env):

# By numeric ID or full URL
.venv/bin/python fetch_task.py 5461
.venv/bin/python fetch_task.py "https://timeline.panteon.no/tasks#/list/15033/edit/5461"

# Also print the rendered markdown to stdout
.venv/bin/python fetch_task.py 5461 --print

# Write under a specific project root instead of PANTEON_TIMELINE_DIR / cwd
.venv/bin/python fetch_task.py 5461 --base-dir /path/to/project

🧪 Development & Testing

# Install dev dependencies (adds pytest)
.venv/bin/python -m pip install -r requirements-dev.txt

# Run the unit tests — no network or credentials required
.venv/bin/python -m pytest

Tests live in tests/ and cover the pure parsing/rendering helpers plus authentication-error handling (via a mocked HTTP session).