About Cartograph
Cartograph is an MCP server for agents that helps teams reuse, improve, and manage code widgets across projects. Its focused workflow tools cover common registry and widget tasks, while the Cartograph CLI remains the source of truth for the full command surface.
Key features
- 01Search, inspect, and install registry widgets
- 02Upgrade or uninstall installed widgets
- 03Create, validate, and check in widgets
- 04Manage workflow defaults and custom rules
- 05Delegate full command surface to Cartograph CLI
Use cases
- 01Find and reuse an existing solution before coding
- 02Install or upgrade a widget in a project
- 03Validate a new widget before checking it in
- 04Adjust defaults and rules for a widget workflow
MCP support
Exposes workflow-oriented tools for registry search and installation, installed-widget management, widget authoring, validation, check-in, configuration, and rules. It depends on `cartograph-cli` and shells out to it for the full command surface.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEcartograph-mcp
mcp-name: io.github.benteigland11/cartograph
Tired of vibe coding the same solutions over and over? Time to stop spending money on redundant tokens and start spending it on innovative solutions.
This MCP server is for Cartograph that exposes the daily widget workflow for agents without mirroring the entire CLI. It targets the MCP 2.0 SDK contract and returns the standard ListToolsResult envelope from tools/list. On installation of the MCP the CLI will be installed automatically. Once you have it, Search, inspect, install, create, validate, check in, custom rules, and configure Cartograph defaults through a compact agent-facing surface, then fall back to the CLI for the full administrative and recovery surface.
It is highly recommended to use the plugins for the skills that go with this MCP. It will give your agent what it needs to explain a lot of the configuration and give you a very powerful workflow tool.
Why this exists
I personally have spent hours and hours working on solutions I'm proud of, only to hit a wall trying to get the same one done. The reality was prompting was never good enough, I needed a way to know my llama.cpp server client integration was going to be the same everytime I used it. I also needed to know that when I found an improvement, that improvement would stick.
If you are sick and tired of wasting money and time on redoing things you've done before, like a mouse on a wheel then get your agents to start using Cartograph.
The Cartograph CLI is the source of truth, but agents do better when the common path is small and explicit.
This MCP keeps the top-level tool surface focused on daily driving:
- finding reusable widgets
- inspecting and installing them
- managing installed widget copies
- creating new widgets
- validating and checking them back in
- adjusting the core Cartograph defaults that affect normal workflow
Everything else stays in the CLI. That keeps the MCP easier to teach, easier to test, and less likely to drift into a second full interface.
Quick start
pip install cartograph-mcp
Claude Desktop example:
{
"mcpServers": {
"cartograph": {
"command": "cartograph-mcp"
}
}
}
The package depends on cartograph-cli and shells out to it as the source of truth for the full command surface.
Common CLI setup commands:
# Claude Code
claude mcp add cartograph --scope user -- cartograph-mcp
# Codex
codex mcp add cartograph -- cartograph-mcp
# Gemini CLI
gemini mcp add cartograph cartograph-mcp
# Cursor
cursor --add-mcp '{"name":"cartograph","command":"cartograph-mcp"}'
Claude Code expects an explicit scope flag such as --scope user.
Tool surface
The MCP intentionally exposes a small workflow-oriented surface:
registry_widgetActions:search,inspect,install,rateinstalled_widgetActions:upgrade,uninstallwidget_statuscreate_widgetvalidate_widgetcheckin_widgetcartograph_configcartograph_rules
These are not a 1:1 mirror of the CLI. They are grouped around agent intent:
- registry-facing work
- installed-widget mutation
- project health/status
- widget authoring
- workflow configuration
- custom validation rules
Example workflow
1. Search the registry before writing logic.
2. Inspect the widget you want to reuse.
3. Install it into the project.
4. If no existing widget fits, create one.
5. Validate it with the full dry-run pipeline.
6. Check it in with a reason once it is ready.
In Cartograph terms:
registry_widgethandles discovery and installinstalled_widgethandles already-installed widget paths likecg/backend_retry_pythonvalidate_widgetis the dry run forcheckin_widgetcartograph_configmanages the defaults that change how your day-to-day loop behavescartograph_rulesmanages custom rules that run during validate and checkin
Philosophy
This MCP is deliberately not the whole CLI.
The common path belongs in MCP. The official full surface belongs in cartograph.
For uncommon, administrative, or recovery operations, use:
cartograph --help
cartograph <command> --help
That includes things like rollback/delete, cloud operations, auth, setup, rules, doctor, export/import, and other non-daily commands.
Configuration
cartograph_config exposes the workflow defaults that matter most to agents:
auto-publishvisibilitygovernancecloudshow-unavailablepublish-registry
Reading and writing config is done through the CLI's --json path so MCP can consume it safely.
Testing
This package is tested in two layers:
- command-contract tests that mock the CLI runner and assert the exact commands the MCP builds
- isolated integration tests that run the real Cartograph CLI in a temporary environment
The integration suite isolates:
HOMEXDG_CONFIG_HOMEXDG_DATA_HOMEXDG_CACHE_HOMEWIDGET_LIBRARY_PATH- project working directory
That means validate/checkin/install flows are exercised without touching the real widget library or user config on the machine running tests.
Development
pip install -e .
pytest -q
The repo includes:
ci.ymlfor normal test/build validation on pushes and pull requestspypi-publish.ymlfor automated release publishing after a successful version-bump CI run
For the full product story and complete CLI surface, see Cartograph.
Comments
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