Provides semantic analysis, AST parsing, and code compaction for C/C++ codebases with intelligent symbol extraction
Enables searching, listing, and analyzing GitHub repositories through cloud integration, providing structured context and graph-based repository analysis
Offers comprehensive semantic analysis, AST parsing, and intelligent code compaction for JavaScript projects with symbol extraction and project navigation
Provides specialized frontend insights and comprehensive analysis for Next.js applications, including architecture detection and React-specific tooling
Supports Node.js project analysis with semantic compaction, dependency analysis, and intelligent navigation hints for server-side JavaScript applications
Integrates with OpenAI APIs for AI-enhanced code analysis, intelligent context generation, detailed code explanations, and embedding-based semantic search
Supports PostgreSQL integration for local development environments when running the full Ambiance server stack
Provides semantic analysis, AST parsing, and intelligent code compaction for Python codebases with comprehensive symbol extraction and project insights
Offers specialized frontend analysis and insights for React applications, including component structure analysis and React-specific architectural patterns
Enables semantic analysis, AST parsing, and code compaction for Rust projects with intelligent symbol extraction and project navigation
Supports Supabase integration for local development environments when running the full Ambiance server stack with local database instances
Provides comprehensive semantic analysis, AST parsing, and intelligent code compaction for TypeScript projects with advanced symbol extraction and type analysis
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Ambiance MCP Server explain the main function in src/index.ts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Unlock smarter coding: 60-80% fewer tokens, deeper insights, and seamless IDE integration
Tired of bloated code contexts wasting your AI tokens and slowing down your workflow? Ambiance MCP delivers intelligent, compressed code analysis that slashes token usage by 60-80% while preserving full semantic depth. Get precise context for debugging, understanding, and navigation—offline-ready, multi-language support, and extensible with AI or cloud features. Boost productivity in your IDE without the overhead.
Use as an MCP tool in your IDE or directly from the command line for flexible integration with your development workflow.
Save Tokens & Costs: Semantic compaction means fewer tokens for AI prompts, reducing expenses and speeding up responses.
Deeper Insights Faster: AST parsing and embeddings uncover hidden patterns, helping you debug issues, trace logic, and grasp project architecture in seconds.
Offline Power: Core features work without internet, keeping you productive anywhere.
Seamless Integration: Plug into your IDE for real-time context, with optional AI enhancements for smarter analysis.
Scalable for Any Project: Handles TypeScript, JavaScript, Python, Go, Rust—whether local or GitHub-based.
Related MCP server: Code Expert MCP Server
npm install -g @jackjackstudios/ambiance-mcpIn your project directory:
cd /path/to/your/project
ambiance-mcp embeddings createThis enables semantic search—takes 2-10 minutes once, then auto-updates on changes.
Add this to your IDE's MCP server settings. Set WORKSPACE_FOLDER to your project path.
Windows:
{
"mcpServers": {
"ambiance": {
"command": "cmd",
"args": ["/c", "npx", "-y", "@jackjackstudios/ambiance-mcp@latest"],
"env": {
"WORKSPACE_FOLDER": "C:\\DevelopmentDirectory\\YourProject",
"USE_LOCAL_EMBEDDINGS": "true"
}
}
}
}macOS/Linux:
{
"mcpServers": {
"ambiance": {
"command": "npx",
"args": ["-y", "@jackjackstudios/ambiance-mcp@latest"],
"env": {
"WORKSPACE_FOLDER": "/path/to/your/project",
"USE_LOCAL_EMBEDDINGS": "true"
}
}
}
}Ambiance auto-activates based on your setup. Add OPENAI_API_KEY for AI boosts or AMBIANCE_API_KEY for GitHub integration.
Semantic Code Compaction: Shrink contexts by 60-80% without losing meaning—ideal for efficient AI interactions and faster coding.
Project Navigation & Hints: Instantly map your codebase structure, spotting key files and patterns to accelerate onboarding and refactoring.
File & Debug Analysis: Extract symbols, explain code, and pinpoint errors using AST—saving hours on troubleshooting.
Embeddings for Similarity Search: Offline semantic queries find relevant code chunks quickly, enhancing accuracy in large projects.
Multi-Language Support: Works across TypeScript, JavaScript, Python, Go, Rust for versatile development.
Set these environment variables in your IDE config or terminal:
Variable | Purpose | Required? | Default |
| Your project path | Yes | Auto-detects if possible |
| Enable offline semantic search | No |
|
| Unlock AI-powered insights | No | - |
| Access GitHub repos | No | - |
For AI: Add OPENAI_BASE_MODEL=gpt-4 (or your preferred model) and set OPENAI_PROVIDER to target a specific vendor.
For embeddings: Set LOCAL_EMBEDDING_MODEL=all-MiniLM-L6-v2 for customization.
AI features now support multiple OpenAI-compatible providers. Set one of the following keys alongside OPENAI_PROVIDER (default: openai):
Provider ( | Primary Key(s) | Notes |
|
| Supports GPT‑5 responses API with caching metadata |
|
| Claude 3.5 / Claude 3 family |
|
| OpenRouter aggregated models |
|
| Grok (xAI) via OpenAI protocol |
|
| Groq hosted Llama models |
|
| Qwen compatible endpoints |
|
| Together.ai models |
|
| Requires |
You can also set a default comparison list with AI_COMPARE_MODELS (comma-separated provider:model pairs) for the CLI comparison utility.
Embeddings generate in the background on first use (with USE_LOCAL_EMBEDDINGS=true), using AST fallback for immediate results. A file watcher auto-updates them every 3 minutes on changes—efficient and incremental.
Manual control via CLI:
ambiance-mcp embeddings status – Check progress and stats.
ambiance-mcp embeddings create --force – Regenerate all.
Use these via your IDE or CLI for targeted analysis.
Core (Offline):
local_context: Compact code for queries like "authentication system".
local_project_hints: Get architecture overviews.
local_file_summary: Analyze files with symbols.
local_debug_context: Debug from error logs.
manage_embeddings: Control embeddings.
AI-Enhanced (Needs OPENAI_API_KEY):
ai_get_context: Smarter context with AI.
ai_project_hints: Deeper insights.
ai_code_explanation: Auto-document code.
Cloud (Needs AMBIANCE_API_KEY):
ambiance_search_github_repos: Find repos.
ambiance_list_github_repos: List yours.
ambiance_get_context: Pull repo context.
Run tools directly for testing or scripts—no IDE needed.
Key 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
Global options: --project-path, --format json, --output file.json, --verbose.
For full options, run ambiance-mcp --help.
Source & contributions: https://github.com/sbarron/AmbianceMCP
Detailed CLI: ambiance-mcp --help --expanded
**Change Log: Version 0.2.4" feat: Major enhancements to embedding management, AI tools, and frontend analysis
Embedding Management & Automation:
Added CLI controls for manual start/stop of automated embeddings updates
Enhanced automatic indexing system with improved background processing
Refactored embedding storage to resolve SQLite memory leak issues
AI Tools Enhancement:
Improved AI-powered project insights with better pattern detection
Enhanced semantic compaction for more efficient code analysis
Updated analysis, explanation, and insights prompt templates
Strengthened local context processing with enhanced semantic understanding
Frontend Analysis Improvements:
Enhanced frontend_insights with better styling file filtering
Added composition analysis for file types in frontend components
Improved environment detection and component analysis capabilities
Infrastructure Updates:
Streamlined CLI documentation with simplified installation instructions
Enhanced tool helper utilities and database evidence processing
Improved project hints functionality for better codebase navigation
**Change Log: Version 0.2.5" feat: Expanded AI provider support, multi-model comparison tool, enhanced debug context analysis
AI Provider Expansion:
Added support for openrouter, grok, and groq providers
Implemented provider-specific API key environment variable priority system
Enhanced provider configuration with fallback API key support
Multi-Model Comparison Tool:
New compare CLI command for side-by-side AI model evaluation
Support for comparing multiple providers and models with the same prompt
Performance metrics, usage statistics, and response comparison
Configurable temperature, max tokens, and system prompts
Debug Context Enhancements:
Improved error context processing with focused embedding queries
Enhanced symbol matching and error type detection
Better semantic relevance ranking for debug assistance
Embedding Management & Automation:
Added CLI controls for manual start/stop of automated embeddings updates
Fixed SQLite memory leak issues in embedding storage
MIT – See LICENSE.
🔍 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:
Structural pattern matching (not just text)
Multi-language support (JS, TS, Python, Go, Rust, etc.)
Wildcard variables ($VAR, $FUNC, $ARGS)
Precise code location information
Fast Rust-based execution with performance optimizations
Comprehensive pattern validation with helpful error messages
120-second timeout for very large projects
Automatic respect for .gitignore files (no manual exclusions needed)
Pattern Syntax:
Use $ + UPPERCASE for wildcards: $FUNC, $VAR, $ARGS
Patterns look like real code: 'function $NAME($ARGS) { $BODY }'
Match specific constructs: 'new $CLASS($ARGS)'
Valid characters: (), {}, [], "", '', numbers, operators, keywords
NOT regex: do NOT use '|', '.*', '.+', '/pattern/', or escapes like '(' or '{'.
Common Mistakes to Avoid: ❌ Don't use: 'function $FUNC' (ambiguous, multiple AST interpretations) ❌ Don't use: 'export $TYPE' (ambiguous, multiple AST interpretations) ❌ Don't use: '$NAME' (too generic, matches everything) ❌ Don't use: /pattern/ (regex syntax not supported)
✅ Good Patterns:
'function $NAME($ARGS) { $BODY }' (complete function structure)
'export const $NAME = $VALUE' (exported constant)
'import $NAME from "$MODULE"' (import statement)
'new $CLASS($ARGS)' (constructor call)
'def ' (Python function definitions)
'class $NAME:' (Python class)
'await $PROMISE' inside 'for ($COND) { $BODY }' (relational patterns)
Examples:
Find all functions: 'function $NAME($ARGS) { $BODY }'
Find all exports: 'export const $NAME = $VALUE'
Find imports: 'import $NAME from "$MODULE"'
Find class instantiation: 'new $CLASS($ARGS)'
Find method calls: '$OBJ.$METHOD($ARGS)'
Find async functions: 'async function $NAME($ARGS) { $BODY }'
Find arrow functions: 'const $NAME = ($ARGS) => $BODY'
Find React components: 'export function $NAME($PROPS) { return $JSX }'
Find Python function definitions: 'def '
Find Python classes: 'class $NAME:'
Advanced Usage:
Use $$$ for zero or more arguments: 'console.log($$$ARGS)'
Use relational rules: 'await $PROMISE' inside 'for ($COND) { $BODY }'
Use multiple searches for OR conditions (alternation not supported)
Direct CLI Usage (for agents with command line access): Agents with command line access can run ast-grep directly:
npx ast-grep --pattern "function $NAME($ARGS) { $BODY }" --lang ts
npx ast-grep --pattern "def " --lang py
npx ast-grep --pattern "class $NAME:" --lang py
npx ast-grep --pattern "def " --lang py src/**/*.py
npx ast-grep --pattern "class $NAME:" --lang py --json=stream
npx ast-grep --help
Use Cases:
Code refactoring and migration
Finding specific patterns across codebase
Security auditing for dangerous patterns
Architecture analysis and dependency tracking
Finding unused imports or exports
API usage analysis
Performance Optimizations for Large Projects:
120-second timeout for very large projects
Automatically respects .gitignore files for exclusions
For additional exclusions, configure .gitignore in your project
Tips for Large Projects (like D:\Dev\SWE-agent):
Use filePattern to search specific directories: "src/**/*.py"
Add large directories to .gitignore: node_modules/, tests/, docs/, etc.
Consider CLI usage for better performance: npx ast-grep --pattern "import json" --lang py src/**/*.py
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | No | AST pattern, not regex. Use $UPPERCASE wildcards. Examples: "$FUNC($ARGS)", "new $CLASS($ARGS)", "import $NAME from "express"" | |
| rulePath | No | Path to an ast-grep rule file (YAML/JSON). When provided, rule mode is used instead of pattern. | |
| ruleYaml | No | Inline ast-grep rule content in YAML (JSON is also valid YAML). Will be written to a temp file and used with --rule. | |
| ruleJson | No | Inline ast-grep rule object (JSON). Optionally validated against local schemas and written to a temp file for --rule. | |
| projectPath | Yes | Project directory path to search in. Can be absolute or relative to workspace. | |
| language | No | Programming language (auto-detected if not provided). Supported: js, ts, py, go, rs, java, c, cpp | |
| filePattern | No | Specific directory or file path to search within the project (e.g., "src", "lib", "*.py", "src/**/*.ts"). **RECOMMENDED for large projects** - if not provided, searches entire project respecting .gitignore. Use wildcards like "src/**/*.py" to search recursively in specific directories. This can dramatically improve performance on large codebases. | |
| maxMatches | No | Maximum number of matches to return (default: 100) | |
| includeContext | No | Include surrounding context lines for each match (default: true) | |
| contextLines | No | Number of context lines to include around matches (default: 3) | |
| respectGitignore | No | Respect .gitignore files and other ignore patterns (default: true) | |
| excludePatterns | No | Additional patterns to exclude from search (e.g., ["test/**", "docs/**"]) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and excels at disclosing behavioral traits. It specifies performance characteristics (120-second timeout, fast Rust-based execution), automatic behaviors (respects .gitignore files), validation behavior (comprehensive pattern validation with helpful error messages), and operational constraints (multi-language support, pattern syntax rules).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long (over 1000 words) with redundant sections. While well-structured with headings, it includes unnecessary content like CLI usage instructions, extensive pattern examples, and performance tips that could be condensed. Many sentences don't earn their place for a tool description aimed at AI agents.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, no output schema, no annotations), the description provides comprehensive context. It covers purpose, usage, behavioral traits, parameter guidance, examples, and edge cases. The only gap is output format details, but with no output schema, the description adequately compensates for all other aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3, but the description adds significant value by explaining parameter relationships and practical usage. It clarifies that 'filePattern' is 'RECOMMENDED for large projects' and provides examples of how parameters work together (e.g., pattern syntax with wildcards, language selection). However, it doesn't fully explain all 12 parameters' interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'structural code search using ast-grep's pattern matching capabilities' and distinguishes it from text-based search. It specifies the verb ('search'), resource ('code'), and unique approach ('structural pattern matching'), making it distinct from any sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (structural code search vs. text-based search) and includes 'Use Cases' section with specific applications. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools, though the structural vs. text distinction is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
🔍 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.
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | Yes | Absolute or relative path to the Next.js project directory | |
| format | No | Output format for the analysis results | structured |
| includeContent | No | Include detailed file content analysis | |
| subtree | No | Frontend directory path to analyze (default: web/app) | web/app |
| maxFiles | No | Maximum number of files to analyze | |
| useEmbeddings | No | Enable embedding-based similarity analysis for enhanced insights | |
| embeddingSimilarityThreshold | No | Similarity threshold for embedding-based matches (lower = more results, higher = more precise) | |
| maxSimilarComponents | No | Maximum number of similar components to analyze per component | |
| analyzePatterns | No | Enable pattern detection for code smells, anti-patterns, and security issues | |
| generateEmbeddingsIfMissing | No | Generate embeddings for project files if they don't exist (may take time for large projects) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'embedding-enhanced analysis' and 'semantic embeddings', hinting at computational intensity, but does not disclose performance implications, rate limits, or error handling. For a tool with 10 parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with a clear purpose and key features. It uses two sentences efficiently, though the second sentence could be slightly more streamlined. There is no wasted text, earning a high score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and high-level functionality but lacks details on output format, error cases, and behavioral constraints. Without an output schema, the agent must infer return values, making this adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the tool uses embeddings for analysis, but it does not explain parameter interactions or provide additional context. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('map', 'analyzes') and resources ('routes, components, data flow, design system, and risks', 'Next.js/React projects'). It distinguishes itself from sibling tools by focusing on embedding-enhanced analysis for architecture insights, component similarities, and potential issues, unlike tools like ast_grep_search or local_file_summary which suggest different scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It mentions analyzing Next.js/React projects but does not specify scenarios, prerequisites, or exclusions. Without context on how this differs from sibling tools like local_project_hints, the agent lacks clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
🚀 Enhanced local context with deterministic query-aware retrieval, AST-grep, and actionable intelligence. Provides: (1) deterministic AnswerDraft, (2) ranked JumpTargets, (3) tight MiniBundle (≤3k tokens), (4) NextActions—all using AST + static heuristics. Optional embedding enhancement when available. Completely offline with zero external dependencies for core functionality.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Query to focus analysis (required for enhanced mode). Example: "How does database connection and local database storage work?" | |
| taskType | No | Type of analysis task - affects query processing and output format | understand |
| maxSimilarChunks | No | Maximum number of semantically similar code chunks to retrieve. Higher values (30-50) provide broader coverage for exploration; lower values (10-15) focus on highly relevant matches. Default 20 balances breadth and relevance. | |
| maxTokens | No | Token budget for mini-bundle assembly | |
| generateEmbeddingsIfMissing | No | Generate embeddings if missing (requires OpenAI API key) - leave false for pure AST mode | |
| useProjectHintsCache | No | Reuse project_hints indices for faster processing | |
| astQueries | No | Optional custom AST queries to supplement automatic detection | |
| attackPlan | No | Analysis strategy: auto-detect from query, or specify: init-read-write (DB/storage), api-route (endpoints), auth (authentication), error-driven (debugging) | auto |
| projectPath | Yes | Project directory path. Required. Can be absolute or relative to workspace. | |
| folderPath | No | Analyze specific folder (falls back to legacy mode if enhanced analysis unavailable) | |
| format | No | Output format: enhanced (new format with jump targets), system-map (architecture overview), structured (legacy), compact, xml | enhanced |
| excludePatterns | No | Additional patterns to exclude from analysis (e.g., ["*.md", "docs/**", "*.test.js"]) | |
| useEmbeddings | No | Use embeddings for similarity search if available (legacy parameter) | |
| embeddingSimilarityThreshold | No | Minimum similarity score (0.0-1.0) for including chunks. Lower values (0.15-0.2) cast a wider net for related code; higher values (0.25-0.35) return only close matches. Use lower thresholds when exploring unfamiliar code. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's 'completely offline with zero external dependencies for core functionality', uses 'AST + static heuristics', provides specific outputs (AnswerDraft, ranked JumpTargets, etc.), and mentions optional embedding enhancement. However, it doesn't cover error handling, performance characteristics, or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with a high-level summary and listing four key outputs. It uses emojis and technical terms efficiently, though some phrases like 'actionable intelligence' are vague. Every sentence contributes, but it could be slightly more streamlined by integrating the offline note earlier.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (14 parameters, no annotations, no output schema), the description is moderately complete. It covers core functionality, offline nature, and output types, but lacks details on return values, error cases, or how outputs are structured. For a sophisticated analysis tool, more behavioral context would be helpful despite the rich schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema—it mentions 'deterministic query-aware retrieval' (hinting at the 'query' parameter) and 'tight MiniBundle (≤3k tokens)' (relating to 'maxTokens'), but doesn't significantly enhance understanding of the 14 parameters. The value added is marginal given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'enhanced local context with deterministic query-aware retrieval, AST-grep, and actionable intelligence' and lists four specific outputs (AnswerDraft, JumpTargets, MiniBundle, NextActions). It distinguishes from siblings by emphasizing AST-based analysis and offline functionality, though it doesn't explicitly contrast with tools like 'local_debug_context' or 'local_file_summary'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for code analysis tasks ('deterministic query-aware retrieval') and mentions optional embedding enhancement, but lacks explicit guidance on when to use this tool versus alternatives like 'ast_grep_search' or 'local_debug_context'. It states 'completely offline with zero external dependencies' which provides some context but not clear when/when-not rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
🐛 Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement
When to use:
When you have error logs, stack traces, or console output to analyze
When debugging complex issues with multiple file involvement
When you need to understand error context across the codebase
Before using AI debugging tools to get structured context
What this does:
Parses error logs to extract file paths, line numbers, symbols, and error types
Extracts focused error contexts (~200 characters) for precise embedding queries
Uses tree-sitter to build symbol indexes for TypeScript/JavaScript/Python files
Searches codebase for symbol matches with surrounding context
ENHANCED: Uses semantic embeddings with focused error contexts for better relevance
Processes each error/warning separately for improved semantic matching
Ranks matches by relevance (severity, recency, frequency, semantic similarity)
Returns comprehensive debug report ready for AI analysis
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
| Name | Required | Description | Default |
|---|---|---|---|
| logText | Yes | Error logs, stack traces, or console output containing error information | |
| projectPath | Yes | Project root directory path. Required. Can be absolute or relative to workspace. | |
| maxMatches | No | Maximum number of matches to return (default: 20) | |
| format | No | Output format preference | structured |
| useEmbeddings | No | Enable embedding-based similarity search for enhanced context (requires local embeddings to be enabled) | |
| embeddingSimilarityThreshold | No | Similarity threshold for embedding-based matches (lower = more results, higher = more precise) | |
| maxSimilarChunks | No | Maximum number of similar code chunks to include from embedding search | |
| generateEmbeddingsIfMissing | No | Generate embeddings for project files if they don't exist (may take time for large projects) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it parses logs, extracts contexts, uses tree-sitter and embeddings, processes errors separately, ranks matches, and returns a report. It also mentions performance ('Fast local analysis, ~1-3 seconds') and embedding features. However, it lacks details on error handling or specific limitations, preventing a perfect score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (e.g., 'When to use', 'What this does'), making it easy to scan. It is appropriately sized for a complex tool, but some sentences could be more concise (e.g., the detailed bullet points in 'What this does' are slightly verbose). Overall, it's front-loaded and efficient, with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no annotations, no output schema), the description is mostly complete. It covers purpose, usage, behaviors, and performance. However, without an output schema, it only briefly mentions the output ('Returns comprehensive debug report'), lacking details on report structure or content, which is a minor gap for such a detailed tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only briefly mentioning 'Input: Error logs or stack traces as text' and 'Output: Structured debug context report,' which are redundant with schema details. Thus, it meets the baseline of 3 without adding significant value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement.' It specifies the verb ('gather'), resource ('debug context'), and method ('from error logs and codebase analysis'), distinguishing it from sibling tools like 'local_context' or 'local_file_summary' which lack the debugging focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes an explicit 'When to use' section with four bullet points detailing specific scenarios (e.g., 'When you have error logs, stack traces, or console output to analyze'), and it mentions using this tool 'Before using AI debugging tools to get structured context,' providing clear guidance on when to use it versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
📄 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).
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | File path for analysis. Can be absolute (recommended) or relative to workspace. Examples: "C:\Dev\my-project\src\index.ts", "/Users/username/project/src/index.ts", or "src/index.ts". | |
| includeSymbols | No | Include detailed symbol information | |
| maxSymbols | No | Maximum number of symbols to return | |
| format | No | Output format preference | structured |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that the tool performs read-only analysis ('Get quick AST-based summary') and mentions path handling behavior, but it doesn't cover potential limitations like file size constraints, supported file types, error conditions, or performance characteristics. The description adds useful context but leaves significant behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that each serve distinct purposes: the first states the core functionality, and the second clarifies path handling. There's no redundant information, and the emoji adds visual distinction without compromising clarity. Every element earns its place in this compact description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, 100% schema coverage, but no annotations or output schema, the description provides adequate basic context about what the tool does and path handling. However, it doesn't compensate for the lack of output schema by describing return values or structure, and with no annotations, it should ideally provide more behavioral context about limitations or error handling to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema by mentioning path flexibility ('Accepts absolute paths or relative paths'), but doesn't provide additional semantic context about parameter interactions or use cases. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get quick AST-based summary and key symbols') and resource ('any file'), distinguishing it from sibling tools like ast_grep_search or local_debug_context by emphasizing file analysis without external dependencies. It provides a precise verb+resource combination that makes the tool's function immediately understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'Fast file analysis without external dependencies' and path flexibility, but it doesn't explicitly state when to use this tool versus alternatives like local_context or frontend_insights. There's no guidance on prerequisites or exclusion criteria, leaving the agent to infer appropriate scenarios from the tool's capabilities alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
📊 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).
| Name | Required | Description | Default |
|---|---|---|---|
| projectPath | No | Project directory path. Can be absolute (recommended) or relative to workspace. Examples: "C:\Dev\my-project", "/Users/username/project", or "." for current workspace. | |
| format | No | Output format preference - structured for detailed analysis, compact for quick overview, json for raw data, markdown for documentation, html for visual reports | compact |
| maxFiles | No | Maximum number of files to analyze for performance | |
| folderPath | No | Analyze specific folder instead of entire project (optional) | |
| includeContent | No | Include file content analysis for deeper insights (may impact performance) | |
| useAI | No | Enable AI-powered folder analysis for better purpose detection (requires OpenAI API key) | |
| maxFileSizeForSymbols | No | Maximum file size in bytes for symbol extraction (performance tuning) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'AI-powered analysis' and 'configurable performance options,' but lacks critical details: whether this tool is read-only or makes changes, what permissions are required, how errors are handled, whether it accesses external APIs (beyond the OpenAI mention), or what the output structure looks like. For a complex 7-parameter tool with AI capabilities, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise and front-loaded, starting with the core purpose. Both sentences contribute value: the first defines capabilities, the second adds path and format context. There's no redundant information, though it could be slightly more structured by separating functional description from technical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, AI capabilities, multiple output formats) and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what the generated 'hints' actually contain, how word clouds or architecture detection work, what the performance implications are, or what happens when AI analysis is enabled without an API key. For a sophisticated analysis tool, this leaves too many operational questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal parameter semantics beyond the schema. It mentions 'absolute paths or relative paths' which aligns with the projectPath parameter, and 'multiple output formats' which aligns with the format parameter. However, with 100% schema description coverage, the schema already documents all 7 parameters thoroughly. The description doesn't provide additional context about parameter interactions or usage patterns beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate intelligent project navigation hints with word clouds, folder analysis, and architecture detection.' It specifies the verb 'generate' and the resource 'project navigation hints' with key capabilities. However, it doesn't explicitly differentiate this from sibling tools like 'local_context' or 'local_file_summary' which might also analyze local projects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal usage guidance. It mentions 'Supports multiple output formats' and 'Accepts absolute paths or relative paths,' but offers no explicit advice on when to use this tool versus alternatives like 'local_context' or 'frontend_insights.' There's no guidance on prerequisites, performance trade-offs, or typical use cases beyond the basic functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
ast_grep_searchfrontend_insightslocal_contextlocal_debug_contextlocal_file_summarylocal_project_hintsEach 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.
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Code intelligence platform for AI agents. 20 tools for architecture, security & impact analysis.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
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