Most of Jumble's features are now part of Automatic.

---

jumble

An MCP server that provides queryable, on-demand project context to LLMs.

The Problem

Large documentation files overload LLM context windows. Even well-structured docs require reading everything upfront, wasting tokens on irrelevant information.

The Solution

Jumble flips the model: instead of loading documentation, an LLM queries for exactly what it needs.

LLM: "What projects are in this workspace?"
     → calls get_workspace_overview()
     → receives: workspace info, all projects, dependency graph

LLM: "What's the test command for my-app"
     → calls get_commands("my-app", "test")
     → receives: "cargo test"
     
LLM: "What files handle authentication?"
     → calls get_architecture("my-app", "authentication")
     → receives: files + one-sentence summary
     
LLM: "What conventions should I follow?"
     → calls get_workspace_conventions()
     → receives: workspace-wide coding standards

Installation

Prebuilt binaries

Prebuilt binaries for common platforms (Linux, macOS, Windows) are available on the GitHub Releases page:

• https://github.com/velvet-tiger/jumble/releases/

Download the archive for your platform, extract it, and point your MCP client at the extracted jumble binary.

From source

cargo install --path .

From crates.io

cargo install jumble

Quick Start

After installing jumble, set up your AI agent integration:

# Setup Warp (creates WARP.md with usage guidance)
jumble setup warp

# Setup Claude Desktop (creates .claude/jumble-usage.md)
jumble setup claude

# Setup Cursor (creates .cursor/jumble-usage.md)
jumble setup cursor

# Setup Windsurf (creates .windsurf/jumble-usage.md)
jumble setup windsurf

# Setup Codex (creates .codex/jumble-usage.md)
jumble setup codex

# Use --global to create config in home directory instead
jumble setup cursor --global

Each setup command will:

• Create usage documentation that guides the AI agent to use jumble effectively
• Check if jumble is configured as an MCP server
• Provide exact copy-paste configuration if needed
• Warn if .jumble/project.toml is missing

Configuration

Jumble discovers projects by scanning for .jumble/project.toml files. It also looks for a .jumble/workspace.toml at the root for workspace-level configuration.

On startup, Jumble also looks for a global configuration file at ~/.jumble/jumble.toml (or the platform-specific equivalent of your home directory). If this file does not exist, Jumble will create it with a minimal

[jumble]

section reserved for future options.

Projects and workspace metadata are loaded once when the server starts and cached in memory. If you change any .jumble/* files, either restart the jumble process or call the reload_workspace tool (see below) to pick up changes without restarting.

Set the root directory via:

1. JUMBLE_ROOT environment variable
2. --root CLI argument
3. Current working directory (default)

Usage with Warp

Recommended: Use the setup command to configure Warp automatically:

jumble setup warp

This creates WARP.md with guidance that tells Warp's AI agent:

• Always start with get_workspace_overview() when entering a workspace
• When to use each jumble tool (before suggesting commands, making changes, etc.)
• How to handle missing context
• Complete workflow examples

You can force-update the jumble section in WARP.md:

jumble setup warp --force

Manual MCP Configuration

Add to your Warp MCP configuration:

{
  "jumble": {
    "command": "jumble",
    "args": ["--root", "/path/to/your/workspace"]
  }
}

Usage with Claude Desktop

Recommended: Use the setup command:

# Project-specific (creates .claude/jumble-usage.md)
jumble setup claude

# Global (creates ~/.claude/jumble-usage.md)
jumble setup claude --global

The setup command creates a comprehensive usage guide and checks if jumble is configured in Claude Desktop.

Manual MCP Configuration

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "jumble": {
      "command": "/path/to/jumble",
      "args": ["--root", "/path/to/your/workspace"]
    }
  }
}

Jumble autodiscovers skills from multiple sources:

• .jumble/skills/*.md - Project-specific flat skills
• ~/.jumble/skills/*.md - Personal/global flat skills shared across projects
• .claude/skills/**/SKILL.md - Claude-style structured skills (project-local)
• ~/.claude/skills/**/SKILL.md - Personal/global Claude-style structured skills
• .codex/skills/**/SKILL.md - Codex-style structured skills (project-local and $HOME)

Structured skills (Claude/Codex format) can include companion resources like scripts/, references/, docs/, assets/, and examples/ subdirectories. When you retrieve a skill with get_skill, companion files are automatically listed.

Usage with Windsurf

Recommended: Use the setup command:

# Project-specific (creates .windsurf/jumble-usage.md)
jumble setup windsurf

# Global (creates ~/.codeium/windsurf/jumble-usage.md)
jumble setup windsurf --global

Manual MCP Configuration

Windsurf's Cascade MCP config uses the same shape as Claude Desktop's config. Create or edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "jumble": {
      "command": "/absolute/path/to/jumble",
      "args": ["--root", "/path/to/your/workspace"]
    }
  }
}

Restart Windsurf so Cascade reloads MCP servers, then verify that jumble appears in the tools list.

Usage with Cursor

Recommended: Use the setup command:

# Project-specific (creates .cursor/jumble-usage.md and .cursor/mcp.json)
jumble setup cursor

# Global (creates ~/.cursor/jumble-usage.md and ~/.cursor/mcp.json)
jumble setup cursor --global

Manual MCP Configuration

Cursor reads MCP configuration from mcp.json in either your project .cursor directory or your home directory.

Global configuration (available in all projects), in ~/.cursor/mcp.json:

{
  "mcpServers": {
    "jumble": {
      "command": "/absolute/path/to/jumble",
      "args": ["--root", "/path/to/your/workspace"]
    }
  }
}

Alternatively, add the same mcpServers block to .cursor/mcp.json in a single project to scope jumble to that project only.

Usage with Codex

Recommended: Use the setup command:

# Project-specific (creates .codex/jumble-usage.md)
jumble setup codex

# Global (creates ~/.codex/jumble-usage.md)
jumble setup codex --global

Manual MCP Configuration

Codex stores MCP configuration in ~/.codex/config.toml.

Using the Codex CLI:

codex mcp add jumble -- /absolute/path/to/jumble --root /path/to/your/workspace

Or edit ~/.codex/config.toml directly:

[mcp_servers.jumble]
command = "/absolute/path/to/jumble"
args = ["--root", "/path/to/your/workspace"]

Restart the Codex IDE extension or TUI and confirm that jumble is listed as an MCP server.

Creating Context Files

Context files are designed to be created by the same AI agents that read them. See AUTHORING.md for the complete guide.

Sample prompt:

Create jumble context for this project.

Read the AUTHORING.md guide at /path/to/jumble/AUTHORING.md, then examine this project's structure to create:

1. .jumble/project.toml (required) - Extract project info from manifest files, identify key commands, map architectural concepts to files
2. .jumble/conventions.toml - Capture patterns to follow and gotchas to avoid (look at existing code patterns, comments, and any constitution.md or similar files)
3. .jumble/docs.toml - Index the docs/ directory if it exists, with one-line summaries

Core Jumble concepts

These fields appear in .jumble/project.toml / .jumble/workspace.toml and are what the MCP tools expose back to the AI.

• Commands ([commands])

  • A small map of named, copy-pastable CLI commands (e.g. build, test, lint, run, dev).
  • Used by get_commands(project, command_type) and get_project_info(..., field: "commands") so the AI can tell you exactly how to build, test, or run a project without guessing.

• Entry points ([entry_points])

  • Labels to the most important source files for a project (e.g. main = "src/main.rs", api = "src/api/mod.rs").
  • Surface in get_project_info(project) so an AI can jump straight to the right file when exploring a new codebase.

• Concepts / architecture ([concepts.*])

  • Each concept describes an architectural area with a short summary and a list of files, for example:
    • concepts.authentication.files = ["src/auth/mod.rs"]
    • concepts.authentication.summary = "JWT-based auth via middleware"
  • get_architecture(project, concept) returns the full description and file list for one concept.
  • get_related_files(project, query) searches across all concepts by name/summary to find related files (e.g. "database", "routing").

• Documentation index (.jumble/docs.toml)

  • A lightweight index of human-written docs (README, design docs, ADRs, etc.) with one-line summaries.
  • get_docs(project) lists all topics and summaries so the AI can pick the right document before reading it.
  • get_docs(project, topic) returns the resolved filesystem path for a single doc.

• Coding conventions and gotchas (.jumble/conventions.toml and workspace [conventions] / [gotchas])

  • Project-level conventions.toml captures patterns to follow and sharp edges to avoid for a single project.
  • Workspace-level [conventions] / [gotchas] in .jumble/workspace.toml describe cross-project standards and pitfalls.
  • get_conventions(project, ...) returns project-specific conventions/gotchas.
  • get_workspace_conventions(...) returns workspace-wide standards or gotchas.

• Related projects ([related_projects])

  • Describes how projects in the same workspace depend on each other:
    • upstream = ["shared-lib"] → projects this one depends on.
    • downstream = ["examples"] → projects that depend on this one.
  • get_workspace_overview() uses this to build a simple textual dependency graph so the AI can see how projects fit together.

• Workspaces (.jumble/workspace.toml)

  • Describes the overall workspace/monorepo: name, description, and shared conventions/gotchas.
  • get_workspace_overview() returns workspace metadata plus the list of all projects.
  • get_workspace_conventions() returns workspace-level conventions and gotchas that apply across multiple projects.

Project Context

Create a .jumble/project.toml in each project:

[project]
name = "my-project"
description = "One-line description"
language = "rust"

[commands]
build = "cargo build --release"
test = "cargo test"
lint = "cargo clippy"

[entry_points]
main = "src/main.rs"

[concepts.authentication]
files = ["src/auth/mod.rs"]
summary = "JWT-based auth via middleware"

[related_projects]
upstream = ["shared-lib"]    # projects this depends on
downstream = ["examples"]    # projects that depend on this

Workspace Context

Create a .jumble/workspace.toml at the workspace root:

[workspace]
name = "My Workspace"
description = "Monorepo for my projects"

[conventions]
error_handling = "Use anyhow for apps, thiserror for libraries"
testing = "Unit tests in same file, integration tests in tests/"

[gotchas]
feature_flags = "Features enabled by one project affect all dependents"

Optional Files

• .jumble/conventions.toml - Project-specific conventions and gotchas
• .jumble/docs.toml - Documentation index with summaries
• .jumble/skills/*.md - Task-specific skills for common operations (project-local)
• ~/.jumble/skills/*.md - Personal/global skills that apply across projects

See AUTHORING.md for the complete guide.

Available Tools

Workspace Tools

get_workspace_overview

Returns workspace info, all projects with descriptions, and dependency graph. Call this first to understand the workspace structure.

get_workspace_overview()

get_workspace_conventions

Returns workspace-level conventions and gotchas that apply across all projects.

get_workspace_conventions()
get_workspace_conventions(category: "gotchas")

reload_workspace

Reloads workspace and project metadata from disk. Use this after editing .jumble files if you want to avoid restarting the MCP server.

reload_workspace()

get_jumble_authoring_prompt

Returns a canonical prompt you can feed to an AI assistant to generate .jumble context files (project, workspace, conventions, docs) for any project.

get_jumble_authoring_prompt()

Project Tools

list_projects

Lists all discovered projects with their descriptions.

get_project_info

Returns metadata about a project (description, language, version, entry points).

get_project_info(project: "my-project")
get_project_info(project: "my-project", field: "dependencies")

get_commands

Returns executable commands for a project.

get_commands(project: "my-project")
get_commands(project: "my-project", command_type: "test")

get_architecture

Returns files and summary for a specific architectural concept.

get_architecture(project: "my-project", concept: "authentication")

get_related_files

Searches concepts and returns matching files.

get_related_files(project: "my-project", query: "database")

get_conventions

Returns project-specific coding conventions and gotchas.

get_conventions(project: "my-project")
get_conventions(project: "my-project", category: "gotchas")

get_docs

Returns documentation index with summaries, or path to a specific doc.

get_docs(project: "my-project")
get_docs(project: "my-project", topic: "configuration")

list_skills / get_skill

Lists or retrieves task-specific skills for common operations.

list_skills(project: "my-project")
get_skill(project: "my-project", topic: "add-endpoint")

AI-Assisted Authoring

Jumble is designed so that an AI can generate context files for any project:

1. schema.json - Machine-readable schema for validation
2. AUTHORING.md - Heuristics for how to populate each field

When asked to "create jumble context for project X", an AI should:

1. Read AUTHORING.md to understand the heuristics
2. Examine the project's manifest files, directory structure, and README
3. Generate .jumble/project.toml (required)
4. Optionally generate conventions.toml, docs.toml, and skills

Schema Validation

Validate your TOML files with the included JSON schema:

# With taplo
taplo check .jumble/project.toml --schema /path/to/jumble/schema.json

License

MIT