Ambiance MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DEBUG | No | Enable debug logging | false |
| NODE_ENV | No | Affects logging behavior (development, test, production) | |
| OPENAI_ORG_ID | No | OpenAI organization ID | |
| OPENAI_API_KEY | No | OpenAI API key for AI-enhanced tools | |
| OPENAI_BASE_URL | No | OpenAI-compatible API endpoint | https://api.openai.com/v1 |
| OPENAI_PROVIDER | No | Provider: openai, qwen, azure, anthropic, together | openai |
| AMBIANCE_API_KEY | No | Ambiance cloud API key for cloud features | |
| AMBIANCE_API_URL | No | Ambiance cloud API URL | https://api.ambiance.dev |
| WORKSPACE_FOLDER | No | Project workspace path | |
| AMBIANCE_BASE_DIR | No | Override working directory | |
| OPENAI_BASE_MODEL | No | Primary model for analysis tasks | gpt-5 |
| OPENAI_MINI_MODEL | No | Faster model for hints/summaries | gpt-5-mini |
| LOCAL_STORAGE_PATH | No | Custom local storage path | ~/.ambiance/embeddings |
| EMBEDDING_BATCH_SIZE | No | Number of texts per embedding batch | 32 |
| USE_LOCAL_EMBEDDINGS | No | Enable local embedding storage | false |
| AMBIANCE_DEVICE_TOKEN | No | Device identification token | |
| LOCAL_EMBEDDING_MODEL | No | Local embedding model when using local embeddings | all-MiniLM-L6-v2 |
| USING_LOCAL_SERVER_URL | No | Use local Ambiance server instead of cloud | |
| EMBEDDING_PARALLEL_MODE | No | Enable parallel embedding generation | false |
| OPENAI_EMBEDDINGS_MODEL | No | Model for generating embeddings | text-embedding-3-large |
| EMBEDDING_MAX_CONCURRENCY | No | Max concurrent API calls for parallel mode | 10 |
| EMBEDDING_RATE_LIMIT_RETRIES | No | Max retries for rate limit errors | 5 |
| EMBEDDING_RATE_LIMIT_BASE_DELAY | No | Base delay for rate limit retries (ms) | 1000 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| local_contextA | π Enhanced local context with deterministic query-aware retrieval, AST-grep, and actionable intelligence. Provides: (1) deterministic AnswerDraft, (2) ranked JumpTargets, (3) tight MiniBundle (β€3k tokens), (4) NextActionsβall using AST + static heuristics. Optional embedding enhancement when available. Completely offline with zero external dependencies for core functionality. |
| local_project_hintsC | π Generate intelligent project navigation hints with word clouds, folder analysis, and architecture detection. Supports multiple output formats including markdown and HTML, with AI-powered analysis and configurable performance options. Accepts absolute paths or relative paths (when workspace can be detected). |
| local_file_summaryA | π Get quick AST-based summary and key symbols for any file. Fast file analysis without external dependencies. Accepts absolute paths or relative paths (when workspace can be detected). |
| frontend_insightsB | π Map routes, components, data flow, design system, and risks in the web layer with embedding-enhanced analysis. Analyzes Next.js/React projects for architecture insights, component similarities, and potential issues using semantic embeddings. |
| local_debug_contextA | π Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement When to use:
What this does:
Input: Error logs or stack traces as text Output: Structured debug context report with ranked matches and semantic insights Performance: Fast local analysis, ~1-3 seconds depending on codebase size Embedding Features: Focused context queries reduce noise and improve relevance |
| ast_grep_searchA | π AST-Grep structural code search tool Performs powerful structural code search using ast-grep's pattern matching capabilities. Unlike text-based search, this matches syntactical AST node structures. Key Features:
Pattern Syntax:
Common Mistakes to Avoid: β Don't use: 'function $FUNC' (ambiguous, multiple AST interpretations) β Don't use: 'export $TYPE' (ambiguous, multiple AST interpretations) β Don't use: '$NAME' (too generic, matches everything) β Don't use: /pattern/ (regex syntax not supported) β Good Patterns:
Examples:
Advanced Usage:
Direct CLI Usage (for agents with command line access): Agents with command line access can run ast-grep directly: Basic usagenpx ast-grep --pattern "function $NAME($ARGS) { $BODY }" --lang ts Python function definitionsnpx ast-grep --pattern "def " --lang py Python classesnpx ast-grep --pattern "class $NAME:" --lang py With file filtering (recommended for large projects)npx ast-grep --pattern "def " --lang py src/**/*.py JSON outputnpx ast-grep --pattern "class $NAME:" --lang py --json=stream Full documentationnpx ast-grep --help Note: ast-grep respects .gitignore files automatically - no --exclude-dir flags neededUse Cases:
Performance Optimizations for Large Projects:
Tips for Large Projects (like D:\Dev\SWE-agent):
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: ast_grep_search is for structural code search, frontend_insights analyzes web architecture, local_context provides general query-aware retrieval, local_debug_context focuses on debugging, local_file_summary gives file-level analysis, and local_project_hints offers project navigation. The descriptions reinforce these unique roles, making tool selection unambiguous.
The naming is mostly consistent with a clear pattern: all tools use snake_case and start with a descriptive prefix (ast_grep_, frontend_, local_) followed by a noun or noun phrase. The minor deviation is that 'frontend_insights' uses a compound noun while others are simpler (e.g., 'local_context'), but this doesn't hinder readability or predictability.
With 6 tools, the count is well-scoped for a code analysis and development assistance server. Each tool serves a specific, valuable function (e.g., search, insights, context, debugging, file summary, project hints), and none feel redundant or out of place, fitting a typical range for such a domain.
The tool set provides comprehensive coverage for code analysis and development workflows: it includes search (ast_grep_search), architectural analysis (frontend_insights), context retrieval (local_context), debugging (local_debug_context), file-level inspection (local_file_summary), and project navigation (local_project_hints). There are no obvious gaps; agents can handle tasks from low-level code examination to high-level project understanding.