MCP Filesystem Server
Provides capabilities to generate call graphs and architectural diagrams in Mermaid format for visual code analysis and documentation.
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., "@MCP Filesystem Serveranalyze the repository complexity and show the directory tree"
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.
RIG MCP Tools
A Model Context Protocol (MCP) server providing intelligent code analysis, graph-based architecture insights, and file operations for AI assistants.
Overview
RIG MCP Tools combines three layers of intelligence:
Static analysis — A Repository Intelligence Graph (RIG) built from AST parsing (ts-morph, tree-sitter) stored in SQLite, enabling graph queries with zero LLM cost.
Semantic search — Embedding-based symbol retrieval using a local model (nomic-embed-text or compatible). Embeddings are cached in SQLite after the first run. Returns precise code snippets instead of whole files — minimizes token usage.
LLM-powered tools — A subset of tools that call a configurable OpenAI-compatible API for natural language reasoning over code.
Installation
From npm
npx rig-mcp-toolsFrom source
git clone <repository-url>
cd rig-mcp-tools
npm install
npm run buildDocker
docker build -t rig-mcp-tools .
docker run rig-mcp-toolsConfiguration
MCP client (Claude Desktop, Cursor, etc.)
{
"mcpServers": {
"rig-tools": {
"command": "node",
"args": ["/path/to/dist/index.js"],
"env": {
"WORKSPACE_PATH": "/your/project",
"GLM_API_URL": "http://localhost:1234/v1/chat/completions",
"GLM_MODEL": "qwen2.5-coder-7b-instruct-mlx@8bit",
"EMBEDDING_API_URL": "http://localhost:1234/v1/embeddings",
"EMBEDDING_MODEL": "text-embedding-nomic-embed-text-v1.5"
}
}
}
}Environment variables
Variable | Default | Description |
|
| Root workspace path |
|
| LLM API endpoint (OpenAI-compatible) |
|
| Model for LLM-powered tools |
|
| Embeddings API endpoint |
|
| Model for semantic search |
LLM and embedding tools are optional — all static analysis tools work without any API.
Available Tools
RIG — Graph Analysis
These tools index the repository into a SQLite graph via AST parsing and query it without calling any LLM.
get_smart_context
Retrieve the most relevant files and symbols for a query using graph centrality and keyword scoring.
{ "rootPath": "/project", "text": "authentication flow" }get_architectural_metrics
Executive summary of repository architecture: core hubs, entry points, and stable foundations ranked by graph centrality.
{ "rootPath": "/project" }graph_analyzer
Component-level complexity analysis with hotspot detection and refactor recommendations.
{ "rootPath": "/project" }generate_call_graph
Generate call graphs or dependency diagrams between components, files, or symbols.
{
"rootPath": "/project",
"level": "component",
"format": "mermaid",
"maxDepth": 5
}level: "component" | "file" | "symbol"format: "mermaid" | "dot" | "json"
generate_diagram
Generate C4 architecture diagrams, sequence diagrams, call graphs, or dependency visualizations from the RIG.
{
"rootPath": "/project",
"type": "c4-container",
"format": "mermaid",
"focus": "auth",
"maxDepth": 3,
"style": "default"
}type: "c4-context" | "c4-container" | "c4-component" | "sequence" | "call-graph" | "dependency-graph"format: "mermaid" | "plantuml" | "dot"style: "default" | "compact" | "detailed"
extract_method
Surgically extract a function or class from a source file to a target file using RIG symbol coordinates.
{
"rootPath": "/project",
"sourceFile": "src/utils/helpers.ts",
"symbolName": "formatDate",
"targetFile": "src/utils/date.ts"
}File Operations
Pure filesystem tools, no graph or LLM required.
read_files
Read content of up to 10 files in a single call.
{ "files": ["src/index.ts", "src/config.ts"] }write_code_unit
Write or overwrite a file with specific content. Creates parent directories as needed.
{ "path": "src/utils/new-file.ts", "content": "export const foo = 1;" }ls_tree
List directory structure as an ASCII tree.
{ "path": "/project/src", "maxDepth": 3 }search_code
Search text or regex patterns recursively across the codebase.
{ "path": "/project/src", "pattern": "useEffect", "useRegex": false }inspect_symbols
Extract class and function signatures from a file using AST analysis (ts-morph).
{ "file": "src/tools/index.ts" }run_shell_task
Execute allowed shell commands.
{ "command": "npm run build", "timeout": 60000 }Allowed prefixes: npm test, npm run, npm list, npx vitest, npx tsc, npx eslint, node --version, tsc, git status, git diff, git log, git show, git blame, ls, pwd, cat, wc.
Quality Analysis
Static analysis tools, no LLM required.
detect_patterns
Detect anti-patterns, code smells, and security issues using Babel AST analysis.
{ "sourceCode": "...", "filePath": "src/auth/login.ts" }suggest_refactor
Detect refactoring opportunities: long functions, deep nesting, magic numbers, duplicate code, and missing type annotations.
{
"file_path": "src/services/user.ts",
"max_suggestions": 10,
"min_priority": 3,
"include_diff": true
}Either file_path or code_snippet must be provided.
analyze_dependencies
Build a lightweight dependency graph of TypeScript files via import analysis. Returns nodes, circular dependencies, and DOT format for Graphviz.
{ "rootPath": "/project/src" }Embedding-Powered
Requires EMBEDDING_API_URL and EMBEDDING_MODEL. Embeddings are generated once per symbol and cached in .rig/index.db — subsequent queries only embed the query string.
search_semantic
Semantic symbol search using vector similarity. Returns the most relevant functions and classes with their code snippets. Use this before read_files to avoid loading entire files into context. Embeddings are generated once per symbol and cached in .rig/index.db.
{
"repoPath": "/project",
"query": "how is authentication handled",
"maxResults": 5,
"threshold": 0.3
}LLM-Powered
Requires GLM_API_URL and GLM_MODEL.
analyze_logic
Ask a natural language question about a piece of code. Uses the configured LLM to reason about behavior, intent, or logic.
{
"filePath": "/project/src/auth/login.ts",
"question": "What edge cases does this miss?"
}Prefer filePath over code to avoid passing file contents through context.
smart_summarize
Generate an intelligent summary of a code file including imports, exports, purpose, and key dependencies.
{ "filePath": "/project/src/services/user.ts", "maxLength": 200 }generate_unit_tests
Generate vitest unit tests for a specific function or class. Uses the RIG index to extract only the method body (not the whole file) — token-efficient. Covers happy path, edge cases, and error cases.
{
"repoPath": "/project",
"symbolName": "createUser",
"filePath": "src/services/user.ts"
}filePath is required only if the symbol exists in multiple files.
investigate_ts_fix
Run tsc --noEmit and use the LLM to explain and suggest minimal fixes for each TypeScript error. Token-efficient: only passes the snippet around the error line (±8 lines), not the whole file.
{
"repoPath": "/project",
"filePath": "src/services/user.ts",
"maxErrors": 5
}filePath and maxErrors are optional. Omit filePath to investigate all errors across the repo.
Architecture
src/
├── cli/ # rig-indexer CLI (pre-index a repo into .rig/index.db)
├── graph/ # RIG graph engine (indexer, parsers, SQLite storage, types)
├── security/ # Path validation and safe extension checks
└── tools/ # MCP tool implementations (19 tools)Pre-indexing a repository
For large codebases, pre-index before using RIG tools:
npx tsx src/cli/index.ts /path/to/project --db /path/to/project/.rig/index.dbOptions: --max-files <n>, --include-tests, --json
Development
npm run build # Compile TypeScript
npm run dev # Run in development mode
npm test # Run test suite
npm run clean # Clean build artifactsLicense
MIT
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