About Cartograph
Cartograph analyzes repositories for coding agents, ranking relevant files and mapping dependencies. Its CLI, MCP server, and packaged agent assets turn a task into reusable, focused context without requiring a model call for static context selection.
Key features
- 01Rank files and map dependencies
- 02Create task-specific work packets
- 03Select static task-matched context
- 04Cache and export run artifacts
- 05Provide CLI, MCP, and agent assets
Use cases
- 01Find files relevant to a bug fix
- 02Prepare context for a repository task
- 03Pass analysis artifacts between agents
MCP support
The MCP server exposes static repository analysis to MCP clients. Its tools analyze repositories, retrieve file contents, and build typed task packets.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
3Saved tool list · updated today
| Parameter | Type | Required | Description |
|---|---|---|---|
| top | number | No | Number of top files to include contents for (default 30) |
| repo | string | Yes | GitHub URL or absolute local directory path |
| max_lines | number | No | Max lines per file (default 200) |
| include_contents | boolean | No | Force embedding top file contents even for compact small-repo mode |
Details
From the project READMECartograph
Cartograph is task-shaped repo context for coding agents. It ships a CLI, an MCP server, user-scope install adapters for Claude Code and OpenClaw, and packaged agent assets for documentation-heavy workflows.
- npm:
@anthony-maio/cartograph - MCP Registry:
io.github.anthony-maio/cartograph - Quickstart:
cartograph.making-minds.ai
Instead of dumping an entire repository into context, Cartograph ranks the files that matter, maps dependencies, caches structured artifacts, and lets the next tool or agent pick up from those artifacts.
The primary workflow is:
analyzeto map the repopacketto prepare the workcontext --staticto load task-matched files without a model call
wiki, host installs, and benchmarks are secondary surfaces built around that core path.
Install
npm install
npm run build
For global use from npm:
npm install -g @anthony-maio/cartograph
For MCP host discovery via the official registry:
io.github.anthony-maio/cartograph- Registry listing
For Claude Code plugin install from this public repo:
/plugin marketplace add anthony-maio/cartograph
/plugin install cartograph@making-minds-tools
For global use from a local checkout:
npm install -g .
See CONTRIBUTING.md for the development workflow and SECURITY.md for vulnerability reporting.
Run a complete example
The npm package includes a small refresh-token example. From a new working directory:
npm init -y
npm install @anthony-maio/[email protected]
npx cartograph analyze ./node_modules/@anthony-maio/cartograph/examples/auth-refresh --static
npx cartograph packet ./node_modules/@anthony-maio/cartograph/examples/auth-refresh --type bug-fix --task "fix refresh token expiry"
npx cartograph context ./node_modules/@anthony-maio/cartograph/examples/auth-refresh --task "fix refresh token expiry" --static --json
node --test ./node_modules/@anthony-maio/cartograph/examples/auth-refresh/tests/auth-refresh.test.js
The packet should point to src/auth/refresh.js and tests/auth-refresh.test.js. The context bundle includes the clock helper and leaves unrelated billing/catalog files out. The three behavioral checks should pass. Walk through the investigation for expected results and how to apply the same process to your repository.
Claude Code Plugin
This repo also acts as a Claude Code plugin marketplace. The cartograph plugin bundles:
- a plugin-scoped Cartograph MCP server
- slash commands:
/cartograph:analyze,/cartograph:context,/cartograph:wiki - the
use-cartographandrepo-surveyorskills - the documentation-agent bundle:
repo-scout,dependency-tracer,context-picker,api-surface-writer, andwiki-writer
Install it with:
/plugin marketplace add anthony-maio/cartograph
/plugin install cartograph@making-minds-tools
Primary Workflow
If you only remember one thing, remember this:
cartograph analyze <repo> --static
cartograph packet <repo> --type <type> --task "<task>"
cartograph context <repo> --task "<task>" --static --json
analyzemaps the repo and tells you what matters.packetturns a concrete job into a reusable working artifact.context --staticselects task-matched files and their nearby dependencies and tests, with their contents. It uses local heuristics and needs no API key.
The separate model-backed context workflow runs when you omit --static; configure a provider before using it. Neither mode proves that a selected file contains the bug.
Command Surface
cartograph analyze <repo> [options]
cartograph packet <repo> --type <type> --task "<task>" [--changed <paths...>]
cartograph context <repo> --task "<task>" [options]
cartograph wiki <repo> [options]
cartograph export <run-id> --to <path> [--artifact <name>]
cartograph install <claude|openclaw|mcp>
cartograph uninstall <claude|openclaw|mcp>
cartograph doctor [target] [--json]
cartograph mcp
Legacy compatibility still works:
cartograph <repo> --static
cartograph <repo> -c "trace auth flow"
CLI Usage
# Map the repo
cartograph analyze ./my-project --static
# Prepare a concrete job
cartograph packet ./my-project --type bug-fix --task "fix auth refresh bug" --changed src/auth/service.ts tests/auth/service.test.ts
# Load the minimum file set for that job
cartograph context ./my-project --task "add user authentication" --static --json
# Force embedded snippets when you really want them
cartograph analyze ./my-project --static --json --include-contents
# Full wiki output
cartograph wiki ./my-project -p gemini -k $CARTOGRAPH_API_KEY -o wiki.md
# Export a cached artifact to an explicit path
cartograph export run-abc123 --to ./artifacts/wiki.md
# Run the MCP server directly
cartograph mcp
For small repos, analyze --static --json now defaults to compact output instead of embedding top-file contents. That keeps tiny repos readable and lets direct file reads stay cheaper than a giant JSON blob. Use --include-contents when you explicitly want embedded snippets.
The default markdown output from analyze --static is human-first: it highlights what matters, surfaces dependency hubs, and recommends the next commands instead of dumping raw file contents immediately.
Providers
geminiopenaiopenrouterollama
Set the API key with --key or CARTOGRAPH_API_KEY. Ollama does not require a key.
Cache Model
Cartograph writes successful runs into the user cache by default:
- Windows:
%USERPROFILE%\\.cartograph\\cache - POSIX:
~/.cartograph/cache
Each run gets a manifest plus named artifacts, which keeps agent handoffs lightweight and makes cartograph export deterministic.
Host Installs
Cartograph uses an explicit hybrid install model. Installing the package does not modify Claude Code, OpenClaw, or MCP host configs automatically.
Instead, install only the integration you want:
cartograph install claude
cartograph install openclaw
cartograph install mcp
What each target installs:
claude: user-scope skills plus the bundled documentation agents under~/.claudeopenclaw: user-scope skill pack under~/.openclawmcp: a Cartograph MCP config snippet under~/.cartograph/mcp
Check status at any time:
cartograph doctor
cartograph doctor --json
Choose Your Surface
- CLI: best default path when you want deterministic local analysis and explicit artifacts.
- Claude Code plugin: best when you want plugin install, bundled skills, slash commands, and a plugin-scoped MCP server.
- OpenClaw skills: best when you want the same Cartograph workflow inside an OpenClaw skill-first environment.
- MCP Registry: best when your host already prefers tool wiring over shell commands.
MCP Server
Cartograph's MCP server exposes static repo analysis directly to hosts that prefer MCP over shell commands.
Tools:
analyze_repo: score files, map dependencies, and return compact analysis output or embedded top-file contents for a local repo or GitHub URLget_file_contents: fetch full contents for specific files after analysisbuild_task_packet: return a typed task packet with key files, dependency hubs, validation targets, risks, and task-specific details
Bug-fix packets are tuned to stay focused when you provide --changed or changed_files: they keep explicit change surfaces in view, prefer exact validation targets, and bias toward shared dependencies over peripheral utility scripts.
If you want Cartograph's packaged MCP snippet, run:
cartograph install mcp
That writes a reusable config file that points at:
{
"mcpServers": {
"cartograph": {
"command": "cartograph",
"args": ["mcp"]
}
}
}
MCP Registry
Cartograph is published in the official MCP Registry with the server name io.github.anthony-maio/cartograph.
Repo-side metadata lives in:
package.jsonvia themcpNamefieldserver.jsonfor registry metadata
The current install artifact published to npm is:
@anthony-maio/cartograph
For repeatable registry releases, this repo also includes a GitHub Actions workflow at
publish-mcp.yml. It is set up for npm trusted publishing on
GitHub Actions, then authenticates to the MCP Registry with GitHub OIDC and publishes server.json.
Packaged Agent Assets
The package currently ships:
- two Claude skills:
use-cartographandrepo-surveyor - five Claude documentation agents:
repo-scout,dependency-tracer,context-picker,api-surface-writer, andwiki-writer - two OpenClaw skills:
use-cartographandrepo-surveyor - bundled OpenProse templates inside both OpenClaw skills for repo-doc and task-context workflows
Skill roles:
use-cartograph: tool-first path when the CLI or MCP server is availablerepo-surveyor: manual fallback path when Cartograph is unavailable or needs verification
Both skills are designed to produce the same downstream contract:
- key files
- dependency hubs
- minimal task context
- doc-ready summary
These assets are meant to pass run IDs and artifact paths between steps instead of copying large prose into the main context.
The Claude plugin marketplace in this repo ships the same skills and agents, plus plugin-first slash commands and a bundled Cartograph MCP server.
Task Packet Benchmark
This repo includes a public benchmark pack for task packets under benchmarks/task-packets.
Use it to compare packet quality across large visible repos with a stable set of task prompts:
npm run benchmark:task-packets -- --list
npm run benchmark:task-packets -- --case llama-cpp-bug-fix --dry-run
The benchmark runner writes packet artifacts to benchmarks/task-packets/output/, which is gitignored.
Curated scorecards and public artifact links are also published on the site:
Example Artifacts
Tracked examples live under docs/examples:
llama-cpp-task-packet.mdllama-cpp-task-packet.jsonllama-cpp-deepwiki.mdtask-packet-benchmark-scorecards.md
These are useful when you want to show what a focused bug-fix packet and a curated repo brief look like on a large public codebase.
Development
npm install
npm test
npm run check
npm run build
npm run pack:smoke
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.