Ambiance

Ambiance MCP Server provides intelligent, compressed code analysis to cut token usage while preserving semantic depth. It offers offline-ready, multi-language support and seamless IDE integration, enabling you to debug, understand, and navigate large codebases faster with optional AI enhancements.

How to use

You connect to Ambiance MCP Server from your MCP client or IDE plugin to gain compact, context-rich analysis of your codebase. Enable embeddings for semantic search, and activate AI-powered insights if you have an API key. Ambiance works offline for core features and supports TypeScript, JavaScript, Python, Go, and Rust.

How to install

Prerequisites: ensure you have Node.js and npm installed on your system. If you plan to run in a Windows environment, you will use a CMD-based invocation; on macOS or Linux, you will run a direct npx call.

# 1) Install globally
npm install -g @jackjackstudios/ambiance-mcp

# 2) Set Up Embeddings (optional but recommended for best results)
cd /path/to/your/project
ambiance-mcp embeddings create
```} ,{

Configure your IDE

Add a server configuration to your MCP client. You can run Ambiance via a stdio (local) server using a one-shot npx command, with the workspace folder and local embeddings settings configured in your environment.

{
  "mcpServers": {
    "ambiance_win": {
      "command": "cmd",
      "args": ["/c", "npx", "-y", "@jackjackstudios/ambiance-mcp@latest"],
      "env": {
        "WORKSPACE_FOLDER": "C:\\DevelopmentDirectory\\YourProject",
        "USE_LOCAL_EMBEDDINGS": "true"
      }
    },
    "ambiance_unix": {
      "command": "npx",
      "args": ["-y", "@jackjackstudios/ambiance-mcp@latest"],
      "env": {
        "WORKSPACE_FOLDER": "/path/to/your/project",
        "USE_LOCAL_EMBEDDINGS": "true"
      }
    }
  }
}
```"}]} ,{

Go

Ambiance auto-activates based on how you configure it. You can optionally provide an API key for AI boosts or a GitHub API key for repository access.

Core features & setup notes

Core features include semantic code compaction, project navigation hints, file and debug analysis, offline embeddings, and multi-language support. You can adapt the setup to your workflow by choosing offline (local embeddings) or AI-assisted modes, depending on your needs.

Advanced usage

Embeddings operate in the background when first used, with a file watcher updating results periodically. You can inspect embedding status and force regeneration via CLI commands, and you can use AI-enhanced queries for deeper insights.

ambiance-mcp embeddings status
ambiance-mcp embeddings create --force
```"}]} ,{

Command-line interface overview

Execute tools directly from the command line to test and automate tasks. Use IO-free operations for local analysis or AI-enabled commands when you supply an API key.

Examples of common commands

ambiance-mcp context --query "How does auth work?" --task-type understand
ambiance-mcp hints --format json --use-ai
ambiance-mcp summary src/index.ts --include-symbols
ambiance-mcp debug "TypeError: undefined"
ambiance-mcp grep "function $NAME($ARGS)" --language typescript
ambiance-mcp compare --prompt "Summarize the new release notes" --models openai:gpt-5,anthropic:claude-3-5-sonnet-latest
```"}]} ,{

Notes on configuration, security, and troubleshooting

Set environment variables in your IDE or shell to control workspace paths and embedding behavior. If you enable AI features, provide a compatible provider and API key according to your preferred provider. Keep sensitive keys secure and rotate them periodically. If you encounter issues with embeddings, verify your local model configuration and ensure the watcher updates correctly.

Available tools

local_context

Compact code snippets for targeted queries like authentication flows or API usage.

local_project_hints

Provide architecture overviews and hints to navigate large projects quickly.

local_file_summary

Analyze files to extract symbols and generate quick summaries.

local_debug_context

Assist debugging by correlating error messages with code paths.

manage_embeddings

Control and monitor the embeddings workflow and updates.

ai_get_context

AI-powered context extraction for richer insights.

ai_project_hints

Deeper AI-driven project structure insights.

ai_code_explanation

Auto-document code with AI explanations.

ambiance_search_github_repos

Cloud search across GitHub repositories.

ambiance_list_github_repos

List repositories accessible to your API key.

ambiance_get_context

Pull repository context from connected repos.